{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 超参数设置\n",
    "epochs = 20\n",
    "batch_size = 64\n",
    "l1_lambda = 0.001  # L1正则化强度\n",
    "lr = 0.001\n",
    "\n",
    "# 数据加载\n",
    "transform = transforms.Compose([transforms.ToTensor()])\n",
    "train_data = datasets.MNIST(\n",
    "    root=\"./data\", train=True, download=True, transform=transform\n",
    ")\n",
    "test_data = datasets.MNIST(\n",
    "    root=\"./data\", train=False, download=True, transform=transform\n",
    ")\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    train_data, batch_size=batch_size, shuffle=True\n",
    ")\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "    test_data, batch_size=batch_size, shuffle=False\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型定义\n",
    "class MLP(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(MLP, self).__init__()\n",
    "        self.layers = nn.Sequential(\n",
    "            nn.Flatten(),\n",
    "            nn.Linear(28 * 28, 512),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(512, 256),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(256, 10),\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.layers(x)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练函数（含L1正则化选项）\n",
    "def train_model(use_l1=True):\n",
    "    model = MLP()\n",
    "    optimizer = optim.Adam(model.parameters(), lr=lr)\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "    train_acc, test_acc = [], []\n",
    "\n",
    "    for epoch in range(epochs):\n",
    "        # 训练阶段\n",
    "        model.train()\n",
    "        correct_train = 0\n",
    "        for data, target in train_loader:\n",
    "            optimizer.zero_grad()\n",
    "            output = model(data)\n",
    "            loss = criterion(output, target)\n",
    "\n",
    "            # 添加L1正则化项[1,5](@ref)\n",
    "            if use_l1:\n",
    "                l1_loss = 0.0\n",
    "                for param in model.parameters():\n",
    "                    l1_loss += torch.norm(param, p=1)\n",
    "                loss += l1_lambda * l1_loss\n",
    "\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            pred = output.argmax(dim=1)\n",
    "            correct_train += pred.eq(target).sum().item()\n",
    "\n",
    "        # 测试阶段\n",
    "        model.eval()\n",
    "        correct_test = 0\n",
    "        with torch.no_grad():\n",
    "            for data, target in test_loader:\n",
    "                output = model(data)\n",
    "                pred = output.argmax(dim=1)\n",
    "                correct_test += pred.eq(target).sum().item()\n",
    "\n",
    "        # 记录准确率\n",
    "        train_acc.append(100.0 * correct_train / len(train_loader.dataset))\n",
    "        test_acc.append(100.0 * correct_test / len(test_loader.dataset))\n",
    "\n",
    "        print(\n",
    "            f\"Epoch {epoch+1}: Train Acc {train_acc[-1]:.2f}% | Test Acc {test_acc[-1]:.2f}%\"\n",
    "        )\n",
    "\n",
    "    return train_acc, test_acc\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Without L1 Regularization ===\n",
      "Epoch 1: Train Acc 92.88% | Test Acc 96.29%\n",
      "Epoch 2: Train Acc 97.21% | Test Acc 97.33%\n",
      "Epoch 3: Train Acc 98.05% | Test Acc 98.01%\n",
      "Epoch 4: Train Acc 98.62% | Test Acc 97.63%\n",
      "Epoch 5: Train Acc 98.87% | Test Acc 97.94%\n",
      "Epoch 6: Train Acc 99.23% | Test Acc 97.58%\n",
      "Epoch 7: Train Acc 99.19% | Test Acc 98.19%\n",
      "Epoch 8: Train Acc 99.45% | Test Acc 97.82%\n",
      "Epoch 9: Train Acc 99.39% | Test Acc 98.10%\n",
      "Epoch 10: Train Acc 99.53% | Test Acc 98.15%\n",
      "Epoch 11: Train Acc 99.54% | Test Acc 98.32%\n",
      "Epoch 12: Train Acc 99.63% | Test Acc 98.10%\n",
      "Epoch 13: Train Acc 99.69% | Test Acc 98.00%\n",
      "Epoch 14: Train Acc 99.62% | Test Acc 97.93%\n",
      "Epoch 15: Train Acc 99.60% | Test Acc 97.90%\n",
      "Epoch 16: Train Acc 99.67% | Test Acc 98.06%\n",
      "Epoch 17: Train Acc 99.77% | Test Acc 97.96%\n",
      "Epoch 18: Train Acc 99.64% | Test Acc 98.16%\n",
      "Epoch 19: Train Acc 99.69% | Test Acc 98.34%\n",
      "Epoch 20: Train Acc 99.79% | Test Acc 98.24%\n",
      "\n",
      "=== With L1 Regularization ===\n",
      "Epoch 1: Train Acc 83.71% | Test Acc 89.39%\n",
      "Epoch 2: Train Acc 89.85% | Test Acc 90.93%\n",
      "Epoch 3: Train Acc 90.74% | Test Acc 91.17%\n",
      "Epoch 4: Train Acc 91.28% | Test Acc 91.58%\n",
      "Epoch 5: Train Acc 91.63% | Test Acc 92.13%\n",
      "Epoch 6: Train Acc 91.96% | Test Acc 92.54%\n",
      "Epoch 7: Train Acc 92.14% | Test Acc 91.98%\n",
      "Epoch 8: Train Acc 92.30% | Test Acc 92.60%\n",
      "Epoch 9: Train Acc 92.40% | Test Acc 92.38%\n",
      "Epoch 10: Train Acc 92.56% | Test Acc 93.04%\n",
      "Epoch 11: Train Acc 92.70% | Test Acc 93.09%\n",
      "Epoch 12: Train Acc 92.83% | Test Acc 92.94%\n",
      "Epoch 13: Train Acc 92.85% | Test Acc 92.70%\n",
      "Epoch 14: Train Acc 92.96% | Test Acc 92.92%\n",
      "Epoch 15: Train Acc 93.08% | Test Acc 93.24%\n",
      "Epoch 16: Train Acc 93.05% | Test Acc 92.56%\n",
      "Epoch 17: Train Acc 93.14% | Test Acc 93.36%\n",
      "Epoch 18: Train Acc 93.19% | Test Acc 93.25%\n",
      "Epoch 19: Train Acc 93.12% | Test Acc 93.43%\n",
      "Epoch 20: Train Acc 93.31% | Test Acc 93.26%\n"
     ]
    }
   ],
   "source": [
    "# 分别训练两种模型\n",
    "print(\"=== Without L1 Regularization ===\")\n",
    "no_l1_train, no_l1_test = train_model(use_l1=False)\n",
    "\n",
    "print(\"\\n=== With L1 Regularization ===\")\n",
    "l1_train, l1_test = train_model(use_l1=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mLrroIvbt28ejjz4aaLU4krPPPps333yTPn36MGjQINatW8d//vOf4+oytmrVKm688UZOPfVUJk2aFLjuq9qoUaPo06cP3bt35+6770ZVVaKjo/niiy/qdB8DAuHh6aef5qqrrsJoNNK7d++ga5uqzZw5k+eee46rrrqKvXv3MnDgQH744Qceeughpk2bxsSJE4/5ddVn//79dV4faC1G3bt3b/LxTj31VK6//npmzZrF2rVrGTt2LDabjYMHD/LDDz8wcOBA/vjHPxIaGsrjjz/Otddey8SJE7nuuutISEggMzOTTZs28eyzzwI1dffII48wdepU9Ho9gwYNwmQy1Tl3ZGQk9913H3//+9+ZOXMml19+OYWFhdx///1YLBb++c9/Nvn1NFZycnKd8NsUjzzyCCeffDJ5eXn079//iPvOmzePl156iRkzZjB8+HAiIiLIysri1VdfZevWrfzjH/+ot36EEKKOthzpQgghWlr1KF8NLdUjwVWPQlbfcqRR7lS1ZtS6Dz/8sN7tI0eOVKOiotSSkhJVVbUR32bPnq2mpKSoRqNRjYuLU0ePHq0++OCDQc/bsmWLOnHiRNVisajR0dHqNddco7711lsqEDS6mM/nUx999FE1IyNDtVgs6rBhw9TvvvuuUaP2FRcXq9dcc40aHx+vWq1WdcyYMerKlSvrPPdIr/HwUfuOVufVtm3bpk6aNEkNCwtTo6Ki1EsuuUTdv3+/Cqj//Oc/g84xZ84cNTk5WdXpdEHnOrycqqqqhYWF6o033qgmJSWpBoNBTUtLU+fMmaM6HI6g/QD15ptvrvN6GhoFsbajjdp3xRVXBPZtyqh91V5//XV15MiRqs1mU0NCQtTu3burM2fOVNeuXRu038KFC9Vx48apNptNtVqtar9+/dRHHnkksN3pdKrXXnutGhcXpyqK0uDoh7W9+uqr6qBBg1STyaRGRESo5513Xp3RIxsqe/VrPZr6Rtc73NFG7TvcjBkzVOCoo/Zt27ZNvf3229Vhw4apcXFxqsFgUKOiotRx48ap8+bNa7A8MmqfEOJwiqoeoU+AEEKIduX666/n3XffpbCwUH41F0IIIdqQdO0TQoh26oEHHiA5OZmMjAwqKir48ssvefXVV7n33nslRAkhhBBtTIKUEEK0U0ajkf/85z9kZWXh8Xjo2bMnTzzxBH/+85/bumhCCCHECU+69gkhhBBCCCFEE8nw50IIIYQQQgjRRBKkhBBCCCGEEKKJJEgJIYQQQgghRBPJYBOAz+cjJyeHsLAwFEVp6+IIIYQQQggh2oiqqpSXl5OcnIxO13C7kwQpICcnh9TU1LYuhhBCCCGEEKKdOHDgAF26dGlwuwQpICwsDNAqKzw8vE3L4na7WbJkCZMnT8ZoNLZpWU4UUuetT+q8dUl9tz6p89Yndd66pL5bn9R56ykrKyM1NTWQERoiQQoC3fnCw8PbRZCyWq2Eh4fLP5JWInXe+qTOW5fUd+uTOm99UuetS+q79Umdt76jXfIjg00IIYQQQgghRBNJkBJCCCGEEEKIJpIgJYQQQgghhBBNJEFKCCGEEEIIIZpIgpQQQgghhBBCNJEEKSGEEEIIIYRoIglSQgghhBBCCNFEEqSEEEIIIYQQoonaNEitWLGCc845h+TkZBRFYcGCBUHbVVVl7ty5JCcnExISwvjx49m6dWvQPk6nk1tuuYXY2FhsNhvnnnsuWVlZrfgqhBBCCCGEECeaNg1SlZWVDB48mGeffbbe7Y8++ihPPPEEzz77LGvWrCExMZFJkyZRXl4e2Oe2227j008/5b333uOHH36goqKCs88+G6/X21ovQwghhBBCCHGCMbTlyadOncrUqVPr3aaqKk899RT33HMPF154IQBvvfUWCQkJzJ8/nxtuuIHS0lJee+015s2bx8SJEwF45513SE1N5ZtvvmHKlCmt9lqEEEIIIYQQJ442DVJHsmfPHg4dOsTkyZMD68xmM+PGjeOnn37ihhtuYN26dbjd7qB9kpOTGTBgAD/99FODQcrpdOJ0OgOPy8rKAHC73bjd7hZ6RY1Tff62LseJROq89Umdty6p79Yndd76pM5bl9R365M6bz2NreN2G6QOHToEQEJCQtD6hIQE9u3bF9jHZDIRFRVVZ5/q59fn4Ycf5v7776+zfsmSJVit1uMterNYunRpWxfhhCN13vqkzluX1HfrkzpvfVLnrUvqu/VJnbe8qqqqRu3XboNUNUVRgh6rqlpn3eGOts+cOXP461//GnhcVlZGamoqkydPJjw8/PgKfJzcbjdLly5l0qRJGI3GNi3LiULqvPVJnbcuqe/WJ3Xe+qTOW5fUd+uTOm891b3VjqbdBqnExERAa3VKSkoKrM/Lywu0UiUmJuJyuSguLg5qlcrLy2P06NENHttsNmM2m+usNxqN7eaD2Z7KcqKQOm99UuetS+q79Umdtz6p89Yl9d36pM5bXmPrt93OI5Wenk5iYmJQ86XL5WL58uWBkHTyySdjNBqD9jl48CBbtmw5YpASQgghhBBCiOPRpi1SFRUVZGZmBh7v2bOHjRs3Eh0dTdeuXbntttt46KGH6NmzJz179uShhx7CarUyY8YMACIiIrjmmmu4/fbbiYmJITo6mr/97W8MHDgwMIqfEEIIIYQQov1QVZUql5dSuztoiQwxMjIjpq2L12htGqTWrl3LhAkTAo+rr1u66qqrePPNN7nzzjux2+3cdNNNFBcXM3LkSJYsWUJYWFjgOU8++SQGg4Hp06djt9s544wzePPNN9Hr9a3+eoQQQgghRPNxeXz+L9kuSu1uSqr8S/WX7yoXJf71pXY3dpeXCKuRuFAzMaEmYmvdxgZuzVhN+qNecy+OTFVV7O5aYajKXScY1beU+W/dXrXOMU/vEy9BqrHGjx+PqtatxGqKojB37lzmzp3b4D4Wi4VnnnmGZ555pgVKKIQQQgghjoeqqlT6Wx9KqlyU1gpCWihyBb6EB0JSlRacKl3eFimTxajzhywzcaEmYmxmYsOqb83E2kzEhpmJsZmIsprQ6Tpn6FJVFYfb13Dw8b8PdRcPpXZXvWGoKYx6hYgQI+EhRiJCjHSPszXTK2sd7XawCSGEEEJ0DC6PD7vbi93lxe72UuXy4HB7qXLVrLO7/I+D9vMCKlaTAZtJj9VswGb23zcZCDUbsJr12EwGbP5bq1mP2SC9Tlqb0+Ol0uml0umhwumhtNLB1mIF96aDVDi9tVqI3P4Wopov4CVVbjy+Y//CrSgQbtG+aEdatdvq+5EhJiKt2hfxyBAjISY9JVVuCiqcFFa4KKhwUuC/Lax0UlDuwu724nD7yCq2k1VsP+r59TqFKKupVouWKRDCardyxYSaiAk1HffnU1VV3F4Vp0crp8PtxenxUWF3sqccftpViBclaJvD/5qCn+PF6fbh8NTd5nB7KXN4KLW7cXl8x1VevU4h0v+eVAei2kv1+3P4+ogQY4dvGZQgJYQQQnRyXp9KhcNDhcuD3eXB7vJR5fLUCTWB8FO93uWlyu3VnlNPMKq+fzxfko+FUa/UCV9Wo47yYh3fVm4mNMSIzaT3h7LaYazmOaFmvf8Y2najvt2Ov3VMqrtdVTg9VDg8VDq1+9VBqPp+pdNDeeB+Q/t4cXnr+7Kth983N7pMJr2OCKsWeLRAZKoViIxEWKu/eJsCX8wjrUbCLEb0zdgiVOXyUFDuIr/CSaE/aGm3TgoqXRSUOyms1MJXSZUbr0/1BzInUH7U44dZDEFdCyOtJrw+X93g4/HhDApCNfcb/idlgC3rmq0uqul1CuEWgxZwrKZaYcdwWPiptc3/ftk6eBg6HhKkhBBCiHbO4/VR4fRQZvdQ5tCuMdBuaz/21Kz33y+vvnV6WqWcep2C1agnxORf/PetgfsGQow6rCYDFqO2XgEqXVorVoXTQ5XTS6VL+wJf5aq+r7WEOP2/nLu9aqC1I5iOLcUHj6nsJoMuEL4sRj16RUGv0xadTsGgU9ArCjodGHQ6dDoFvQJ6nQ69Dv++OvQKNfvrFHSKdl/nf371Mets8z9Hd9g+1c9RoU7ACYQkl4eK6tYihz/8uDxH+DJ+7EKM+kAg9Tor6ZoYQ5TVfFhA0r5wR1qDW44sRl27+MJtNRnoGmOga4z1qPu6vT6KKmu1bJX7W7ZqtXZVh7DCChcen0q5w0O5w8PugspmKa/ZoMNi1GM26PC6HERHhBJiMtRar8ds1GEx6LEYa/a1GLXH5sPWm416LAY94bVCUqjZ0C7em45GgpQQQgjRwtxeX02ocTQyDPnXlzu0L8zNwWTQYTXpsRr1WI4ScKpDUEj148OCkbafIWg/k6FlW3U8Xl8gdFWHq0qXFr5Kq5z8sn4jGb36YvdoI4JVt7jUBDUvVf6AVh1GqlvTXB4fLo+P4qrDw1nHpigQajIQavG3yPlb42z+daGBdbXv6wP7hplrnmcz6TH4W+7cbjcLFy5k2rRhnXpOI6NeR0K4hYRwy1H3VVUt4Ae6Evpvi6tcGPW6WuGm4aBjqRWKzEbtOdUBp6bOT+3Udd6RSJASQgjRIdhdXvLKHeSVO8ktc5BX5iSv3EleuQOnW2upUFFRVbSl+j7aY4Ieq/79qbtOrf841ftT59hq0HFQVXyqSn6Rnoe2Lqfc4fFfC3T8rCY94RYj4SEG/62RcIvBfxu8PswSvE+YxdjiQaelGfQ6IkJ0RITU/RLpdrsxZG9g2qndmvQl0+Xx1QQyf8ByuL34fODx+fCpKl6f1j3S61Pxqio+n4rHp916VbVmm0977z3V9w/frqp4vTXHCH4ueH0+vCratupz+PcB/EFHT6jZGBR2gkOQfx9/SAoxnrjdrlqboihat0SriR7xoW1dHNEKJEgJIYRoM6qqUuH0aIGoTAtFgdvD1rVW97Tmo4DdGbTGZtIHhZ4wS8NBqL5g1Nmu42kPTAYdJoOJKJuprYsihOhgJEgJIUQ7oqoqeeVOth0sIzO3AhW1VtcrPSEmrftHSK1uVRaTTttmrOl209ZUVaXM7iGv3EHuYcEot9xBfq11TWmtsRi1bjbxYWbiwyzEhZmJDzdjM2n/O1MUUPx3lMBjpWZ9rXX+/1Bq7+vfqfo5HL69nmNRa131vl6fl03r1zJp7KlEh1kItxgJlSAkhBCdigQpIYRoIw63l525Ffx2qIzfD5bz28Eyfj9UdlzXaJj0OixGXa3rWQxYDAqVZTo+K9qA1WwIXP9iqX39i7/ffshh18YEbv33LUY9ZXa3v0ud1sUuv9xJXlmtLnf+bU0ZUjfUbNDCUbgWkILu17oN6yAXRLvdbhy7VAakhMu1DEII0UlJkBJCiBamqioHSx38fqiM3wKBqZzd+RX1jqqlUyAjLpTeiWGY9bqaYaf9w+PaD5uLx+72Bq7hcXl9uLw+yhyHd4PTsaM0v8Vf6+HCLQatBenwUBRmJj7MHNhmNcn/joQQQnQs8n8uIYRoRnaXl+255fzuD0vVoanuMM2aKKuRvknh9EkMp29SGH2TwukRH4rF2PgJHVVVxenxBQUre605gSrsLlatWUef/gNxejliIDt8Xe15hdRaoS/aZvK3GtWEourHCeE13e6a8jqEEEKIjkSClBBCHANVVckqtvP7IS00VXfP21NYGRQ4qhl0Ct3jQunjD0t9ErXb+DDzcXdVUxQlMKRuVD3b3W437r0q04Z1OeZuZtVhzeH2YjUZOvzob0IIIcTxkiAlhBBHUen0sD3X37p0sJzf/aGpoVHkYmwm+iZpLUx9EsPpkxRGj/hQzIaO2zpTO6wJIYQQQoKUEKKVlDvc/LKniFW7CtmUVUJRoZ7389ZiMRow6XX+IYj9i3/iwur7tbdVT2oYtL7WfW2b/rDnKJj0uqO2/Ph8WivTNv+gD78fLOe3Q2XsK6yqd3+jXqFHfBh9E8NqtTSFExdmbokqFEIIIUQ7IkFKCNEiqlwe1uwtZtWuQlbtLmRzVslhAyso7CovatUyNRS+tJAFewsqqWxgKO74MDN9ksLp6++S1ycpjIzYUOniJoQQQpygJEgJIZqFw+1l/b5iVu0uDLQ6ub3BFwt1i7FySvcYhnSJYNuWTQwYOBgvCi6PD6fHh9ur4vL4cHm92q1HG4HOWX3f//iI92ut8xw2JF71iHYEz5EaxKTX0TMhNOg6pj6JYcSESiuTEEIIIWpIkBJCHBOXx8fGAyX+FqcC1u8vqTNvUEpkCKd0j+GUjBhO6R5DcmQIoA1+YD64kWknJbfoHDten4q7dhCrHbwCgU3F5fXh8froGm2lW6xNJk0VQgghxFFJkBJCNIrH6+PX7FJW7Srk592FrNlbhMMdHJwSws2B0HRKRiyp0SFtOnmqXqeg18kACUIIIYRofhKkhBD18vpUtuWUsWp3Aat2FfLLnqI61w/F2EyM8rc4je4eQ3qsrU2DkxBCCCFEa5EgJYQAtBHrtueWBwaHWL27kDJH8PDeESFGRmVEa8GpRyw940MlOAkhhBDihCRBSogTlKqq7MqvCASnn3cXUVTpCtonzGxgRHo0p3SPYVRGDP2SwtHpJDgJIYQQQkiQEuIEoaoq+wqrAqPqrdpdSH558PB1VpOeYd2iA131+ieHY5CBF4QQQggh6pAgJUQn5fL42JVfwZbs0kB4OljqCNrHbNBxclqUv6teDIO6RMqIdUIIIYQQjSBBSogOzudTOVBcxfZD5dqSq93uKaisM4+SUa8wJDWKUd21FqeTUiNlRDshhBBCiGMgQUqIDkJVVfIrnDWB6VA5O3LL2ZFbgd3trfc5YRYDfRLDGN4tmtHdYzk5LYoQkwQnIYQQQojjJUFKiHao3OFmR2452w9VsP1QWaCVqbjKXe/+JoOOnvGh9E4Io3diGL0Sw+iTGEZiuEVG1RNCCCGEaAESpIRoQ06Pl115lezILed3fwvT9kPlZJfY691fp0C3GJsWlvyhqXdiGGnRVhkUQgghhBCiFUmQEqIV+Hwq+4uqAi1Lta9j8h52HVO1xHBLIChVtzT1iA+Va5qEEEIIIdoBCVJCNCNVVckvd9YEJn9o2nmE65jCLQb6JIbTKzGU3onhWmhKCCPCamzl0gshhBBCiMaSICXEcVJVlY0HSpi/ej/f/Jbb4HVMZoOOngmh9ErQrl/SbsNJCDfLdUxCCCGEEB2MBCkhjlGF08OCDdnMX72fbQfLAut1CnSLtdUKS9ptWowNvU4CkxBCCCFEZyBBSogm2pJdyv9W7+fzjdlUurTueiaDjrMHJnHp8FQGy9xMQgghhBCdngQpIRqhyuXhi005zF+9n01ZpYH1GXE2rhiZxkVDU4i0mtqwhEIIIYQQojVJkBLiCH4/VMb81fv5dH025U4PAEa9wpkDkrhiZFdGpkfL9U1CCCGEECcgCVJCHMbh9rJw80H+t3o/6/YVB9anxViZMaIrF5/chZhQcxuWUAghhBBCtDUJUkL47cqv5IN1OXy8PotSuzbynkGnMKlfAleMTGN09xh0MliEEEIIIYRAgpQ4wTk9Xhb+epDnturJXPVjYH1KZAiXj0hl+rBU4sMtbVhCIYQQQojj5PNBRS6U7NeWsmwwhkBINIRE+ZdI7dYSAToZNKsxJEiJE9K+wkrm/7KfD9dmUVTpAhR0CpzeJ4ErRnVlbM84GapcCNEwVYWqIijdDyUHoDQLSg9oX1BKszCUZjHZ7UNf8hrE9vIvPbUlPAXk2kohRHNSVags8Aelvdpt8T7/433a3ymvs5EHU7QwFQhYTVj0J1a0OLFerTihub0+vtmWy/9W7+eHzILA+oRwM0PCq/j7ZRPoGhvWhiUUQrQbXg+U59QKSYcFptIscFc1+HQFCAHYs1xbajPaILYHxPQMDlgxPbRfiIUQ4nCqCvZiIqr2oPz+BZRl1bQulfgD0xH+JgGg6CC8C0SlQXgyeJxgL/YvJWAvAlcFoIKjRFuK9zStnObwmpatpiyGjnntuQQp0ellFVfx3i8HeH/tAfLLtV9jFAXG9YrjipFpjMmIZMnir0mKkC58QpwwXJUNh6SSA1qIUn1HP05oAkSkQmQqRHSBiK4Q0QW3LZFVK79ndK84DMW7oGAnFO6Eot3groSDm7QliKId5/CAFdtLO4+0YgnRuTnKakJRUGuS9tjoKmc8wPaGDqBoASmyK0SmabdR/tvIrlpruN545DJ4XFqACgSsRi4O/9QwzjJtKdnftNdutGqBKn0sXPBi057bhiRIiU7J4/Xx/fZ8/rd6H8t35KOq2vrYUDOXDu/CZcO7khptBcDtdrdhSUW7cGgz/P6V9j+a9LHaF2LRcVV3cSk9UBOMDut6h73o6MfRGbXPQmSqFpaCApP/tqFfUd1uim0HUAdPA2OtLy5eNxTv1YJVwY5atzu0Ly/VvzDv+jb4eOZwrcXq8IAVndFhf8kV4oTjqqwnJO2reewoOeohHIYITAm90EUdHpTSjvw3qbEMJgiN15am8Hm1MFVfyKoqOkIAK9F+tHJXaYu95PjK38okSIlO5WCpnffXHOC9Xw5wqMwRWD+mRyxXjOzKxH4JGPW6NiyhaDe8Hti+EFa/BPt+CN4W3R0yxkH6OOh2Gthi2qaMJxqvBzx2cDuaeGv3X0RdHZiytPVHY44IDka1W5UiU8EWD7pm/nuhN9YEIabVrFdVqCo8LFz5W7GK92q/8Oas15baFJ32BerwgBXbC6wx0oolRGuq/hGnaHfdpWQ/VBUc/RjWmHpak7pBZFfctkQWL/2eadOmoTMepWWpten0YI3Wlqbw+bS/b9XBytCxegdJkBIdntensmJnPvNX7+fb33Lx+Vufom0mLjm5C5eP6Eq3WFvbFlK0H1VFsP5tWPOq1kIBoOih52SozIOcDVC0S1vWvg4okDhAC1UZ46HrKWAObctX0Ha8Hu2LfVVRrSDj0ILMMd0eFoh8nmYsrAJhiYeFpMNalSwRzXi+46QoYIvVlrTRwds8Tu2L2OEBq2Cn9gWkeI+27Fwc/DxLZK2BLvytWZFda65JMFolaAnRVKqq/XBTOyQV7vLf3wOu8iM/3xJZKySlBYemiNQj//+lM/ag0en811RFAultXJima/dBqry8nPvuu49PP/2UvLw8hgwZwtNPP83w4cMBqKio4O6772bBggUUFhbSrVs3br31Vv74xz+2cclFS8srd/Dh2izmr95PdknNr88j06O5YlQaU/onYDbI8J3CL3er1vr06wc1rRUh0TBsFgy7BiJStHX2Etj3I+xeDntWQP5vWte/Q5th1bOgM0DKsJoWqy7Dta4QnY3Hpb32nI3+63k2anXocRztmc3DYNEWY8hRbi1gCNECSO2QFJ7Sebq8GcwQ31dbaqv+Qnd4N8HCnVrrnKMEsn7RlvroTUe4+Duy1v3o4G3mMAlgJzp/y0uo4yBU5EFYXOf6O+jzaddJ1mlZ2uO/zvFIgzoo2t+i6HSt6210hnY/qpsWmNrTDzjiuLX7IHXttdeyZcsW5s2bR3JyMu+88w4TJ05k27ZtpKSk8Je//IXvv/+ed955h27durFkyRJuuukmkpOTOe+889q6+KIFFFY4+c/i7Xy0LguPv/kpIsTIRUO7MGNkKj3iZeQ94efzwvZFsPpF2LuyZn3CQBh1Iwy4qO4oaSGR0OcsbQEoz9UCVfXoayX74cDP2rL8Ee1X/a6j/C1W4yBxUMebf8PtgLytWmCqDk5528DrqruvKQzCErTwYjw87Fhrgk2Tbg8LSHpz83ep64wUf6tbWCKknxa8zVWltaoW7Ay+Dqv8kHZ9mM+jvb8VudrSpPPqmzgiV6TMTdORqP4R20qztbmGSrOgLKfW/Wwoy8HocXAGwG93ac8zhdYTwhtaaoVzYxt15fJ5tddTXze8oj1HHipc0WmhKBCUai2RaW33mkSra9dBym638/HHH/PZZ58xduxYAObOncuCBQt44YUXePDBB1m1ahVXXXUV48ePB+D666/npZdeYu3atQ0GKafTidNZ8w+krKwM0AYdaOuBB6rP39blaI88Xh/vrsniw29/5ArPAi4z7CXH1peIfpMYNGYaltAooOl1J3Xe+lq8zu0l6Da9g27t6yil2shBqqJD7X02vuHXoaaOqvlF/WhlsERD3/O1BaB4L8relej2rkDZ9wNKZT7s+k5bANUSiZp2Kmq3sfi6jdUGCGjjX++D6ttdhZK3DeXgJpRDv6Ic3AQFv6PU061OtUSiJg7SlqTBqImDtV9VlRYOOV6vtnRgbf53RTFCTB9t6X3YNlXVRg60l4C9GMVRXOu+/7b6IvBa67CXoHjsoHq1az0ac71H7dPWmptGtUQGvnSr5gitO5MpFEw2VFPNfUyhqP7bwLoGuiS2eZ13FM5yKMtBKdOCklKWjVKWA+XV63JQ3JWNOpRbZ8Hgc6KgasNmuypqukw3kmoICYSvwOfCEonqD1pBnxVLrXButB39b6vXDaUHUIr3oBRpXWCV4t0oxXugeB+Kr+HPiqozQGRX1KgM1OgMiEpHjUpHjU7XWpz0R2iBa6HPoHzGW09j61hR1erxzNqf8vJywsPD+eabbzjjjDMC60855RTMZjPLli3jxhtvZN26dSxYsIDk5GSWLVvGueeey6JFixgzZky9x507dy73339/nfXz58/HarW22OsRxy6zDNbtPsjFni84V/cTBiV4WGIfOkpsGeSFDSA/rD/Ftu6oSrv+naD1qGqbf5FvLWH2LNLzl5Ja/CMGn9aa4tLb2Bs7gb2xp2M3xTbvCVWVMEcWceXbiC3fRmzFbxh9wV3f7MYoCkL7kh/Wn/ywfjhMrTdwhd7rIMK+j8iqfUTY9xBZtY8wR7b2pecwTn0opdZulFjTKQlJo9SaTpUp9oT57IjG0flcmLyVGD2VmLwVmDwVGL2VtW4rMXorgm5N3koMh/27OB4qCh6dBY/egkdnwasz49aHaPf1Zjy6EP+2w+7rQ/DqzP7HIYF1Hp255X8caCV6n5MQVxEWdxEhriJC3IXaba37Rl8jBmJB+5tgN0VjN8bgMEVjN0YHHttN0TiMUfh0RlB9GL1V/s9FxWG31Z8T/+eh1jqjpxIdjZhioAE+RY9LH4rbYMOltwXuexUjVlchNuchrK6CI57DqxioMsdTYUqg0hxPpTkhsNhNMaiKtKCeqKqqqpgxYwalpaWEh4c3uF+7DlIAo0ePxmQyMX/+fBISEnj33XeZOXMmPXv2ZPv27bhcLq677jrefvttDAYDOp2OV199lSuvvLLBY9bXIpWamkpBQcERK6s1uN1uli5dyqRJkzC2txFZ2sChMgfvfvYFg/a8zpn6NYH13ozTUftfiJKzHt2e5ShFu4Kep5psqF1Ho6aPx5c+DmJ7N/iFsFPVudcNedvQZa9DyVmLkr1WG/Ervj++bmNQ08agpp4Clk70Ofd5UTKXoFvzCrq9KwKr1fh+eIdfj9q/nu57LcXnQTm4EWXvSpS9K1AO/IJyWPcQNSodX7exqOljUdPGaCM0NQdHGUruZpRDtVqaCjPrDU2qLd7f0jTY39I0SLumSEJTs+lUf1eag9eltXo5SrTWLn+Ll1I9/4y7EsXpb9FwVQZuFVftdZX1fp6bg2q0BreCGa2gN2itDjqDNhS+3r/oTKj6uuuos86I6r+tvY7D1qn1rKv3vtcd3Grk725X06KUrdVnY16vJQLCklHDU1DDkyE8BTU8BcK1dYQlaS1/R3Dcn3HV34rl/zxoLZ9F/s9HCTiKUfwtpUGfG3vxEVuS6pzGYAluTYpKr2llCkvqUN1N5e9K6ykrKyM2NvaoQard/2Q/b948Zs+eTUpKCnq9nqFDhzJjxgzWr9eGgP3vf//Lzz//zOeff05aWhorVqzgpptuIikpiYkTJ9Z7TLPZjNlc9yJko9HYbj6Y7aksbcHl8bHoq4+IWf8Mdyq/gh58KHh6nYVp/B3ok0/SdjzZH5hLDsDuZbD7e9i9DKWqECVzKWQuRQ8QmqiNuNZ9gnYtS3hSnXN2yDovy4GstZC1RrvN2VD/sM+5m9HnbobVL2i/vCadpF1T0W2sdn1PG41Cd1x1bi+BDe/ALy9r83CA9tr6nAUjbkDpNgZDqwcDI3Q7RVu4Uxud7sAv2rVVu5dDznqU4j3oi/fAhre0pyQO1D6T6eO00doa817Yi2smdK2+pumwHxMCwpLxJQ5kR7mVHqddhCF1GEpYIoqEplbRIf+utASjESw2IOXYj+HzzzVTHbSc5bVCVwWeqlJ+27SGft27ovdU1QQwZ4U2klrgfqX22FmhdVMElOo5bPw92jr0vw5TqPbDSIQWjAjv4r+f4h+IJRnFrF1L3Byv87g+46ZoCG3icNmq6p9vqIF5iVxV2qAz/muWlNBE0Ok69nt6GPm70vIaW7/tPkh1796d5cuXU1lZSVlZGUlJSVx66aWkp6djt9v5+9//zqeffspZZ2kXhg8aNIiNGzfy2GOPNRikRDumqmxe/jHqysc5z7sNFPCio6znBURNvgtT3OGd/f0iU2Holdri80Hulppgte8nqDgEv76nLQBxfWuCVfLw1np1x8dt174wZ62pCU5l2XX3s0Roo8p1Ga4t0elawNqzQhtwoWh3zXw0Pz6t/dqaPNQfrE6D1JFgasddXPO3a6PvbXpPu84DtOFkT74Khl+rXQDcXhhDtAEoMsbBGWi/vO/7yT8i4HJtQIf6RgRMH6s9p8tw7cvewY3+xR+cqoPj4SJSIWmwFpSTT9Luh8bjdbvZvnAh3XudGTw5rBAdiU6n/dBgDgUS6mxW3W52H4yhz2nT0Dfmc66q2tDyh4cyZ4X2Rd3n1lqBvO7g+16Xf7CO2vdd9ex72LZ693Np0wrUvu91aY/rY7DUCkld6g9LlojO3bqsKP6WQ5tMni7aXLsPUtVsNhs2m43i4mIWL17Mo48+GhgcQnfY6E56vR6f79j73Yo24PNRsOYjqr59hIGuTACcGMlKu5D0c+cQFdOEuQV0OkgapC2n3qqNSHZgdU2wytmoDeuc/xusfgGDzsCpId3RhW2FnhO1UKFv438aqqrNCxNobVqjfdk+fEAARQcJ/WtCU8owbXCDw0c8i+kOAy/W7pdma4Fqz0rYu0Ibha56iOSVj2vdSLoM00JV+mnQZUTbj0Dk88HOJdroe7u/r1kf1xdG3gCDLm3f4a+aJQJ6T9UW0IYNDowIuELrhlk9IuCKR7X3oqEvVFHdakJT9a1MHCxE4ymK9rfNaNGGz29PVLUmrFUHM0WnDbTQmUOSEB1Muw9SixcvRlVVevfuTWZmJnfccQe9e/dm1qxZGI1Gxo0bxx133EFISAhpaWksX76ct99+myeeeKKtiy4aw+vGtfF9Kr75D7H2vQBUqmZ+TbyQAZfcQ/fY1OM/h9FS0yrAP7XJRPesqOkGWLyX2MrtsOIRbTGHayGi+wSt1ao1Rl1zlEL2+prglL0Wqgrr7meLh9QRWtDpMlz78tzUbnkRKTD4Mm0BKN5XK1it1Fq59q/SlhWPakNRp46oCVYpw1pvvhBHKWz4n9Z9r3iPf6UCvadpASp9bMf+UhEarwXc6pBbvK+mG+CeFdoEwQDR3WtamJJO0n4kCIlqq1ILIVqaotRcIyWEaLfafZAqLS1lzpw5ZGVlER0dzUUXXcS///3vQN/F9957jzlz5nDFFVdQVFREWloa//73v7nxxhvbuOTiiNwO1A3v4Fj2BCFV2UQDpaqVb8IuYNDFd3NKtxbsnmWNhv7nawvgztvJ1i+eY5CtUBuswFEC27/SFtC6TNS+vio07vjO7/Nq3dNqd9HL/x0Ov4hab9K+OHcZXhOcIlKbPzhEpWnLkD/UtIRVh6o9K7VukXv9j5ehzf3TdaQ/WI2F5CHN/z/7gp1aeNo4X+tqA2CO0LpujrhOa43pjKLSIGomDJ3pfy/2ap9XmcBRCCGEaHfafZCaPn0606dPb3B7YmIib7zxRiuWSBwXZwWsfR3Pj89gqMojBMhXw3nfcB49pt3KhUN7tv5F8FHd2Bc7gf7TpqHT67TrUPytVez/GcqyYOM72gLaZK4Z47Rg1XX00buUVeRrLUzVoSl7vXah8+Ei02q66HUZDokDwFB3UJQWpSg1kwqefJX2Zb4ws+b6qj0rtbljdi/TFtDm8ug6qmbwiqTBx9Y10ueDzG/gl5e022pxfWp137M1x6vsGBRFu75NCCGEEO1Suw9SopOoKoJfXkb9+UUURzEGIFuN4TXfOYSeMosbzhiAzdwOPo46PaQM1ZbTbtdG/9m/qiZYHdoMuf5l1bNaq1HqyJoWq/h+2gACta9tKt5b9zxGm3aOQHAapnXzam8UBWJ7asvwa7Rglf97zfVVe3/QRkna9a22gNY1suspNYNXJA488vCyjjLY9K42gERg5DkFep2pBaiM8R27+54QQgghOqV28M1VdGrlufDzc6hrXkNxVaAAu3xJvOg9h6Lu53PvuSeRHtuOWxlMVuhxhraA1rq0Z7kWrHYt01qrqru9ffevho8T10cLS9Wj6cX37VBzVwQoilb2+L4w8nqtFSlva01XwL0/grMUdi7WFtC6paWNqQlW8f0AsDkOoVvydy1EVbfQmcNhyJUw4lqtVUwIIYQQop2SICVaRsl++PG/sGEeeBwowDZfGs95zmNLxFjuO2cQZ/SN73hz2YTG1QwOoKpQuKumtWrPCnCWaYMA1G5pSh4KIZFtXfKWodNpLU6JA+GUm7Trvw79WhOs9q3SBoyofc1ZSDT66AwmZq+tOU5sLxhxPQy+vM3mtBJCCCGEaAoJUqJ5FeyEH56EX98PDNW93teTZz3n8ZP+ZG46oyePj83AYuyArTGHUxSI7aEtI67T5v+ozIewxBO3K5pOrw0+kTxEG3re69HmP6q+vmr/z2AvQpddBICvxyR0o/4IGRPqDtkuhBBCCNGOSZASzePgr9ocRNs+o3r0udUM5CnXuazy9WPqgCS+OasvXaI6wFw/x0pvgPCkti5F+6I3+EccHAZj/qLNhZK9Hs+hLSzb42HchbPRyQSxQgghhOiAJEiJ43PgF1jxWM31MMBq0ygeLp/GRrUH3eNszDu3P6f1PM4hw0XnoDdC15GoSUOpPLiwrUsjhBBCCHHMJEiJplNV7ZqglY9rXbYAVdGxKeJ07s6byO+OrthMev4+sSdXj07HZJAuW0IIIYQQonORICUaz+eDHYu0AJW9DgBVZyQz6Wz+mj2BzYdiAbhgSAp3T+1DQrilLUsrhBBCCCFEi5EgJY7O64Gtn8IPT2hzJAEYQsjteSl35IxjxS5t0tg+iWE8cN4ARqRHt2FhhRBCCCGEaHkSpMSRFe6Cj2bBwU3aY3M4lYOv5pHi03l7QxUA4RYDf5vSmxkjumLQSzc+IYQQQgjR+UmQEg3b9D589VdwVYAlEu+om3nHO4X/rDhEhbMKRYHLhqfyt8m9iQk1t3VphRBCCCGEaDUSpERdzgpY+DfY9K72OG0Mvwx9hL9/W0hmXhYAg1MjeeDc/gxOjWy7cgohhBBCCNFGJEiJYAc3wYezoGgXKDoYP4cnHGfz33f3ABBjM3HXmX24+OQu6HQn6KSzQgghhBDihCdBSmhUFVa/CEv/AV4XhKfARa9SmTiCl/61FICZp6Rx+6TeRFhlAlUhhBBCCHFikyAloLIQPrtZG9ocoPdZcN6zYI3m+19zcHp8dIuxcv+5/VEUaYUSQgghhBBCgtSJbu8P8PF1UJ4DehNMeQiGXwv+wLRo8yEApg5MkhAlhBBCCCGEnwSpE5XXAyv+AyseBdUHMT3h4tchaVBgF7vLy3e/5wEwbUBSW5VUCCGEEEKIdkeC1ImoNBs+uQ72/ag9PukPMO1RMNmCdlu+Iw+720uXqBAGpIS3QUGFEEIIIYRonyRInWh+Xwif3QT2YjCFwtlPwqDp9e660N+tb5p06xNCCCGEECKIBKkThduhjcj3y0va46STtK58Md3r3d3h9vLtb7kATB2Q2EqFFEIIIYQQomOQIHUiKNgJH82CQ5u1x6f8Cc74JxhMDT7lh50FVLq8JEdYOEkm3RVCCCGEECKIBKnOTFVh07vw1d/AXQnWGDj/Reg1+ahPXbjlIABnDpBufUIIIYQQQhxOglRn5SyHr26HX9/XHqePhQtehvCjj77n8vhYus3frW+gdOsTQgghhBDicBKkOqOcDfDRbCjaDYoeJsyBMX8Fnb5RT/9xVwHlDg/xYWZO7hrVwoUVQgghhBCi45Eg1ZmoKvz8PCz9J/jcEJEKF70KXUc16TCLNld360tEp5NufUIIIYQQQhxOglRnUVkAC26CnYu1x33OhvOehZCmtSi5vT6WVHfrk0l4hRBCCCGEqJcEqc5gzwr4+DqoOAR6M5z5EAy7Bo5hkIifdxdSUuUmxmZiRHp0CxRWCCGEEEKIjk+CVEfm9cDyR2DFfwAVYntrc0MlDjjmQ1ZPwjtlQCJ66dYnhBBCCCFEvSRIdVQlB+CT62D/Ku3xkCth6iNgsh3zIb0+lSVbtSA1Tbr1CSGEEEII0SAJUh3Rb1/AZ38CRwmYwuCcp2Dgxcd92F/2FFFY6SLKamRkhnTrE0IIIYQQoiESpDoStwOW3AtrXtEeJw/VuvJFpzfL4Rf5J+Gd3C8Ro17XLMcUQgghhBCiM5Ig1VHkb9fmhsrdoj0efSucfh8YTM1yeJ9PZdEWrVvfmTIJrxBCCCGEEEckQaq9U1XY+D9YeAe4q8AaCxe+BD0mNutp1u0vJr/cSZjFwKndY5v12EIIIYQQQnQ2EqTaM0cZfPkX2PKR9jhjPFzwEoQ1f4vRQv8kvJP6JWAySLc+IYQQQgghjkSCVDul5KyHBddD8V5Q9HD6vXDqbaBr/pDj86l8vUVG6xNCCCGEEKKxJEi1N6qP7rmL0L/1Ifg8ENEVLn4NUke02Ck3ZpVwsNRBqNnAmJ7SrU8IIYQQQoijkSDVnlQVof/4OgbkfKM97ncenPNfCIls0dMu8nfrO6NvPBajvkXPJYQQQgghRGcgQao9MZhRSvbhVYyoZ/4fhhHXgKK06ClVVWXhZq1b31Tp1ieEEEIIIUSjyKgC7YnJhufC11ne+37UoVe1eIgC2JJdRnaJHatJz/jecS1+PiGEEEIIIToDCVLtTXxfykO6tNrpFvon4Z3QW7r1CSGEEEII0VgSpE5gqqoGro+aKpPwCiGEEEII0WjtPkiVl5dz2223kZaWRkhICKNHj2bNmjVB+/z222+ce+65REREEBYWxqhRo9i/f38blbjj+O1gOXsLqzAbdEzoHd/WxRFCCCGEEKLDaPdB6tprr2Xp0qXMmzePzZs3M3nyZCZOnEh2djYAu3btYsyYMfTp04dly5axadMm7rvvPiwWSxuXvP1b5O/WN753HDazjDsihBBCCCFEY7Xrb892u52PP/6Yzz77jLFjxwIwd+5cFixYwAsvvMCDDz7IPffcw7Rp03j00UcDz8vIyGirIncYqqrylb9b37SBMlqfEEIIIYQQTdGug5TH48Hr9dZpXQoJCeGHH37A5/Px1VdfceeddzJlyhQ2bNhAeno6c+bM4fzzz2/wuE6nE6fTGXhcVlYGgNvtxu12t8hraazq87d0OXbmVrA7vxKjXuG07tFt/rrbUmvVuaghdd66pL5bn9R565M6b11S361P6rz1NLaOFVVV1RYuy3EZPXo0JpOJ+fPnk5CQwLvvvsvMmTPp2bMny5cvJykpCavVyoMPPsiECRP4+uuv+fvf/87333/PuHHj6j3m3Llzuf/+++usnz9/PlartaVfUruw6IDC11l6BkT5uK6Pr62LI4QQQgghRLtQVVXFjBkzKC0tJTw8vMH92n2Q2rVrF7Nnz2bFihXo9XqGDh1Kr169WL9+Pd988w0pKSlcfvnlzJ8/P/Ccc889F5vNxrvvvlvvMetrkUpNTaWgoOCIldUa3G43S5cuZdKkSRiNxhY7z1nP/MSOvAoevXAAFwxJbrHzdAStVeeihtR565L6bn1S561P6rx1SX23Pqnz1lNWVkZsbOxRg1S77toH0L17d5YvX05lZSVlZWUkJSVx6aWXkp6eTmxsLAaDgX79+gU9p2/fvvzwww8NHtNsNmM2m+usNxqN7eaD2ZJl2ZVfwY68Cox6hSkDktvNa25r7en9P1FInbcuqe/WJ3Xe+qTOW5fUd+uTOm95ja3fdj9qXzWbzUZSUhLFxcUsXryY8847D5PJxPDhw9m+fXvQvjt27CAtLa2NStr+fb3lEACju8cSYZV/iEIIIYQQQjRVu2+RWrx4Maqq0rt3bzIzM7njjjvo3bs3s2bNAuCOO+7g0ksvZezYsYFrpL744guWLVvWtgVvxxYGRuuTSXiFEEIIIYQ4Fu2+Raq0tJSbb76ZPn36MHPmTMaMGcOSJUsCTW4XXHABL774Io8++igDBw7k1Vdf5eOPP2bMmDFtXPL2aV9hJVtzytDrFCb1kyAlhBBCCCHEsWj3LVLTp09n+vTpR9xn9uzZzJ49u5VK1LEt8nfrOyUjhmibqY1LI4QQQgghRMfU7lukRPNa5O/WN1W69QkhhBBCCHHMJEidQLKKq9iUVYpOgcnSrU8IIYQQQohjJkHqBFI9Wt+I9GjiwuoO/y6EEEIIIYRoHAlSJ5Ca0fqS2rgkQgghhBBCdGwSpE4Qh0odrN9fAsCU/tKtTwghhBBCiOMhQeoE8fUWrTVqWFoUCeGWNi6NEEIIIYQQHZsEqRPEQv/1UVOlW58QQgghhBDHTYLUCSCv3MGavUUAnDlAuvUJIYQQQghxvCRInQAWb81FVeGk1EhSIkPaujhCCCGEEEJ0eBKkTgCLAqP1SWuUEEIIIYQQzUGCVCdXWOHk592FAEwdINdHCSGEEEII0RwkSHVyS7bl4lNhYEoEqdHWti6OEEIIIYQQnYIEqU5ukX+0PhlkQgghhBBCiOYjQaoTK6ly8VNmAQBTJUgJIYQQQgjRbCRIdWJLt+Xi8an0SQwjIy60rYsjhBBCCCFEpyFBqhOr7tY3TSbhFUIIIYQQollJkOqkyhxuVu7MB2TYcyGEEEIIIZqbBKlO6tvfcnF7VXrGh9IjPqytiyOEEEIIIUSnIkGqk1q4WevWN1W69QkhhBBCCNHsJEh1QhVOD8t3SLc+IYQQQgghWooEqU7ou9/zcHl8ZMTa6J0g3fqEEEIIIYRobhKkOqGvtxwEtEl4FUVp49IIIYQQQgjR+UiQ6mSqXB6+/726W59cHyWEEEIIIURLkCDVySzfno/d7SU1OoT+yeFtXRwhhBBCCCE6JQlSnczC6kl4ByRJtz4hhBBCCCFaiASpTsTh9vLdb7mADHsuhBBCCCFES5Ig1Yms2JFPpctLcoSFwV0i2ro4QgghhBBCdFoSpDqRRVtqJuGVbn1CCCGEEEK0HAlSnYTT4+WbbVq3PpmEVwghhBBCiJYlQaqT+DGzgHKnh4RwM0NSo9q6OEIIIYQQQnRqEqQ6iUWbtW59Z/ZPRKeTbn1CCCGEEEK0JAlSnYDb62PJNhmtTwghhBBCiNYiQaoTWLWrkFK7m9hQE8O7Rbd1cYQQQgghhOj0JEh1Aou2HARgSv9E9NKtTwghhBBCiBYnQaqD83h9LN5aPVqfdOsTQgghhBCiNUiQ6uB+2VNEUaWLKKuRkenSrU8IIYQQQojWIEGqg1tYq1ufQS9vpxBCCCGEEK1Bvnl3YF6fytdbtG59Zw6QSXiFEEIIIYRoLRKkOrC1e4soqHASbjEwuntsWxdHCCGEEEKIE4YEqQ5s0RZtEt5J/RIxGeStFEIIIYQQorXIt+8OyudT+dofpKYNlG59QgghhBBCtKZ2H6TKy8u57bbbSEtLIyQkhNGjR7NmzZp6973hhhtQFIWnnnqqdQvZBjYcKOFQmYNQs4ExPaVbnxBCCCGEEK2p3Qepa6+9lqVLlzJv3jw2b97M5MmTmThxItnZ2UH7LViwgNWrV5OcnNxGJW1dizZro/VN7BuP2aBv49IIIYQQQghxYmnXQcput/Pxxx/z6KOPMnbsWHr06MHcuXNJT0/nhRdeCOyXnZ3Nn/70J/73v/9hNBrbsMStQ1XVwPVRU2USXiGEEEIIIVqdoa0LcCQejwev14vFYglaHxISwg8//ACAz+fjyiuv5I477qB///6NOq7T6cTpdAYel5WVAeB2u3G73c1U+mNTff4jlePXrFKyS+xYTXpGp0e2eZk7usbUuWheUuetS+q79Umdtz6p89Yl9d36pM5bT2PrWFFVVW3hshyX0aNHYzKZmD9/PgkJCbz77rvMnDmTnj17sn37dh5++GG+//57Fi9ejKIodOvWjdtuu43bbrutwWPOnTuX+++/v876+fPnY7VaW/DVNI/P9+n4NkfHkBgfV/fytXVxhBBCCCGE6DSqqqqYMWMGpaWlhIeHN7hfu26RApg3bx6zZ88mJSUFvV7P0KFDmTFjBuvXr2fdunU8/fTTrF+/HkVRGn3MOXPm8Ne//jXwuKysjNTUVCZPnnzEymoNbrebpUuXMmnSpHq7KaqqymNP/gDYmTXxJKbKRLzH7Wh1Lpqf1HnrkvpufVLnrU/qvHVJfbc+qfPWU91b7WjafZDq3r07y5cvp7KykrKyMpKSkrj00ktJT09n5cqV5OXl0bVr18D+Xq+X22+/naeeeoq9e/fWe0yz2YzZbK6z3mg0tpsPZkNl2ZJdyoFiOxajjjP6JWE0tvu3sMNoT+//iULqvHVJfbc+qfPWJ3XeuqS+W5/UectrbP12mG/hNpsNm81GcXExixcv5tFHH+Wiiy5i4sSJQftNmTKFK6+8klmzZrVRSVtW9dxR43vFYzN3mLdPCCGEEEKITqXdfxNfvHgxqqrSu3dvMjMzueOOO+jduzezZs3CaDQSExMTtL/RaCQxMZHevXu3UYlbjqqqLPQPez5VJuEVQgghhBCizbTr4c8BSktLufnmm+nTpw8zZ85kzJgxLFmy5IRs0tyRW8HugkpMBh2n94lv6+IIIYQQQghxwmr3LVLTp09n+vTpjd6/oeuiOoPq1qixPeMIs5x4QVIIIYQQQoj2ot23SIkai7ZoQWqadOsTQgghhBCiTUmQ6iAy88rZkVuBUa9wRt+Eti6OEEIIIYQQJzQJUh3Eos3aaH2n9oglIkS69QkhhBBCCNGWJEh1EAv9w55PG5DUxiURQgghhBBCSJDqAPYWVPLbwTL0OoVJ/aRbnxBCCCGEEG1NglQHsMjfGjW6ewxRNlMbl0YIIYQQQgghQaoDqB6tb6p06xNCCCGEEMdIVVU8xcU4fv8dX1VVWxenw2v380id6A4UVfFrVik6BSb3l259QgghhBDi6OybNlG1fgPurCzc2dm4s7NwZeeg+gNU+icfY+nXD4CyRYuoWLYMQ3IyxuRkjEnVt4noQkLa8mW0a00KUqqqsnz5clauXMnevXupqqoiLi6OIUOGMHHiRFJTU1uqnCesr/3d+kamxxAbam7j0gghhBBCtG+e/HxcWVnoIyMxREWhCw9H0XWeTli+ykocublaOPKHJFeWdj/15ZcxJsQDUL50KYWvvlbvMfRRURiTkwOPq9atp/Szz+vfNzqatHfmYc7IAMC+dSuegwcxJCVhTE5GHxmJoijN/Co7hkYFKbvdzpNPPsnzzz9PYWEhgwcPJiUlhZCQEDIzM1mwYAHXXXcdkydP5h//+AejRo1q6XKfMBbKJLxCCCGEEA3yuVwoOh2KQftaW7F8OQfvva9mB50OfWQk+qgo9JGRxP/lNqzDhgHg2ruXqg0b0UdGYIiK0vaJikIXFtZm4cBXVYU7JweXPyRFnH02+ogIAGKWLGH3XXc3+Fx31oFAkAo56STCp03DmJKiLV26YEzRWpp05uAf58OnTMYQH4/7YA7unBw8OTm4s3PwVVXhLSpCHxUV2Lf0k08p/t//Ao8VqxWjP1QZk5OJu/UWDDExgdeimEyB96bB16z6cHgcWI3WplVWG2tUkOrVqxcjR47kxRdfZMqUKRiNdecx2rdvH/Pnz+fSSy/l3nvv5brrrmv2wp5ockrsbNhfgqLAlP4SpIQQQgghqqmqSvnSpeQ99jjRV80k+oortA16A8bUVLzFxfgqKsDnw1tUhLeoCACf0xk4RuWaNRy67x91D67Xo4+MJOnfDxI2fjwAjt9+o2zR1/6wFRkUvPRRUehstkaFL5/TiaLXBwW/kgULcGfn4M7OxltYGLS/pW9frEOHAuAJC9eKFxlZKxylYOySgqlLF8w9egSeFzZxImETJzaqLq3Dh2MdPjxonaqq+MrKcOfkoI+MDKw3JiViGTQId04O3oIC1KoqXLt24dq1C4C42/4c2DfviScpfvddjAkJGJK1sFUWZeGAtYrsUDebUtzsdeSQVZHF1PSp/OvUfzWqvO1Fo4LUokWLGDBgwBH3SUtLY86cOdx+++3s27evWQp3oqvu1jcsLYr4cEsbl0YIIYQQbcGdm4evsgJTevoJ24XqcPbNW8h95P+wr10HQMn7HxA1YwaKohB5wflEXnA+AKrLhaekBG9xCd7iYrwlxVj69g0cxxATi23MGP82bR9fVRV4vXgLC9GZakZLtm/ZQuHLLzdYppQnHid82jQAqtato+jteeijItHZbHjy8gPd8Dx5eaTNn4916BAAXNnZlC/6OuhYurCwQAuSYqppPSobOoRT7roTc60WopaiKAr6iIhAa1i1mGuvJebaawEtFHoOHsSdk4P74EGqsg+Q6csla986DpQfIHXbMlK8Xm17Tg52tPcr1b+8/Gc9FVbtM21YtxVObfGX1awaFaSOFqJqM5lM9OzZ85gLJGrIaH1CCCHEicm5ezcFzz2PfcMG3Dk5gNZVK+bGGwgdN+6EDVTuQ4fIf/LJwPU8isVCzOxZxFxzTb11ophMGOPjMcbH13u8sNMnEHb6hKB1PqczEKqMXWqu/zdnZBD1hz9ooau4GE9JdfgqQbXbg7q/OXftonzx4oZfR3Y2+IOUbfhw4u++C2NKCiZ/K5M+PLze56lmM7rQ0AaP29JUVaXIUcSB8gP0jOqJzWzD1K0bC5y/8NyB5yiKKIIva67LUiapRI7W83ifu+nljMKdk8PeHWvJ37ONyCq4bfzNpIZ3JTUsFdvKTW32uo7VMY/a5/F4eOmll1i2bBler5dTTz2Vm2++GYtFWk6aQ165k7X7igE4c4B06xNCCCE6I09xMfYNG7Fv3Iilf3/Cp0wGQNHpKPvqK20nnQ70euwbN5J14x8x9+lD0v1zCRk8uA1L3vpKP/uMg/+ci+pwABBx3rnE3XYbxqTG/+Ds9rlxepw4vA4cHgdOr3Y/PTw9cH3Orqr9bCnbggMHzr0/Y/facXqc2r5neLi6/y2khacBsHjvYt7a+hZeexVVWf+i6kMnDo+DmEN2ek3SMSPpbFKUSAxxcWw1FfJqwWfY48JR1LcIW/wpNqONUGMotj42Lug5gn4x2ih6uZW5/Fb0W2B7qDEUs2LGo3qauVYbtr9sP6sPreZA+QGyyrPYX7afA+UHqPJoo/69Nvk1RiSNAMCoM1Lk0LpORpojSQ1LpUtYF1LDUuka1pVuyaOJsMYBEEvN5T+n1Dqfe3jHG1TtmIPUrbfeyo4dO7jwwgtxu928/fbbrF27lnfffbc5y3fCWrotF1WFIV0jSY6UYSeFEEKIjk71+XBmZgaCk33DBlx79wa2h515ZiBIGdPSiLv9r4QMGIBl4CB8VZUUvfkWxe+9h3P7dnRh9bdYdEZOr5MyZxmliVZUp5OQk08m4e672Rrn4Ku8LynPLqfMVUa5q5wKdwUOjwO7x87j4x4nNVxrUXr515d5YeMLDQaR+dPmMzBuIAA/ZP/AE+ueaLA8Z3Y7MxCkShwlbC7YXGefsmjYE61j2oQpDO16OgCHtn/Ahp/LwVcOxXWPOyppVCBIrc9bz50r7qz3/A+99xD/OvVfTMvQuhFuyt/Eq7++SqgptCZ41bp/UtxJgXpwep1UuCqwGCzkVuZyoPwA+8u1gHSg/AA3Db4pUA9rDq3hgVUP1Dm/gkKCLQG7xx5YN7bLWN47+z1Sw1IJNx3bZ7OhVsP2rNFB6tNPP+WCCy4IPF6yZAnbt29Hr9cDMGXKFBmtrxl9vTUXgGnSrU8IIdolb0UFvspKVIcDn8OJ6nTgczhQnS7clZUobndbF1G0MW9FBZ78fMzp6YB2vc6eCy8CT/CXeVNGBiFDTiJ0zJjAOkVRiL3uOqrcVeQ5S0mKTyLhzjuIue5a8lZ8Q0mCFaOjCIPOQPkTz2LukkrURRejb4dz/rh9bspd5YGlzFUWCD7lrnIu631ZoDXo3d/f5cvdX1LmLCNxeyFx2RV8MUwNHOvzN/5L2sgzUBSFn9b/l1c2v9LgecvcZYH7OkVXJ0RZ9BYsBgtmfXBLSFp4GmO7jMWsNwftYzFYsOgtJIfWDBt+asqp/HfCfzEbzIQYQgLPMRvMmPVmIkw11xedmX4mg+MGU+mupMJdEbitcFVQ4a4gIzIjsK/NaGNg7EBtP5e2X3VLkMvnwqSvuXYrpyKHZVnLGqyHB0Y/EAhSvxz8hZu+vanBfSenTQ4EqZ5RPTkt5TRSw1KDlpSwlDp1FhMSQ0xITIPH7awaHaRee+013nrrLZ577jlSUlIYOnQoN954IxdddBFut5tXXnmF4YeN9iGOTbkbftkr3fqEEKIxVJ8P1W7H53SiOhyoXi+mWvMaVq1bhyc/PxBytMCj7YtOR9yfbg7sm/f00zi2bPWHI4d26z8uikLP5csC+2bffjuVy1c0XLB/1fySe+jBf1O5ciWGhAQMCQkYE+IxxNfctwwYcNThgUX7pqoq7v37qdqwQWtx2rAB586dWPr1I/3jjwDQWSxYTz4Zj8+DOrAXiSPHETJ4MPrISD7c8SHbCn+h8LtFFDoKKbQXUuQowu6xYzVYWX3FagAMUVH8J/wnvvlI+3wlFKk8/ZYXnQo7n3yIRSMNzPm/5YSERwPwyC+PsDxrOQadAaPOGFiqHz8x/glCTdo1NwsyF7A+d33QvgadAaNeuz+jz4zAvpsLNrPWuZbC3wqp9FbWCUrPnP4MEeaIQBne3/5+g3U3setEuhq7ApBflU/e9k384XsfI3aoeHSwNl3PoRgdYaYwXL1SA9dCDYgdwAU9LiDMFBa0VAea1LCavwOX9r6UczLO0cKQwYJJZ2rwOrPTu57O6f5WpKPpEtaFLmFdGrVvuCmc8OjGtdaM7TKWsV3GBq1zOB18tugzRo8fTYytJrQMiBnA3FPmBsJZuas8KKylhKYE9q3dimQz2gLhqLob3skJJwe2D4obxPMTn29UeU9Ujf6r/eWXX/Lee+8xfvx4br31Vl5++WX+9a9/cc899wSukZo7d24LFvXEsblIwafCoC4RpEZ3rPH0hRCitXiKiyn+33yK588PDGsMYIiLo+fKmoCT99jj2DdsqPcYOqs1KEg5Nm+h8ocfGjynqqqBL186swX0enRmM4rFgmIxozNbUCwWdOHhqLVG+3Lt2xdY6tN744ZAkCp48UXsv27GkBCvDRkcn1BzPyEBXWjoCTvQQHuV889/UrZkCWpxSZ1teQX7eGzxjRS4i7VwdEYhbtVDiGEHv4yrGXZ72YFlrMiqP5j7VB9OrzPQCqAoCgbFgEf1UBQOr0/Scd7PPuLK4PLvPOyfOJXoK68k6g9XUGAv4ED5gUa9jvW56/k089MGt1/Q44JAkFq0dxEL7Aug/n9alDpLA0GququXzWgj3BQeFHrCTeEYddq0Ot6SEiZ/ls3Ej1QUrwp6HdYLz+aDK/5EeHwKOiV4Ut2mBJ7q83Vkep0ei2Ih0ZYYNBVRanhqoMXpaCZ3m8ymtE04PA5CDCHyt+Q4Nennr8suu4wzzzyTO+64gylTpvDSSy/x+OOPt1TZTlgbC7UPtYzWJ4QQ9ct/7jkKX30N1W4PWq+YTCi1AgyApW8f0OtqQo4/+OgsZnTW4B+roq+6ivCzzkJnMaOYtX0UiwXFbEZ32GBKKU89iaIL/mJXze12s2nhwsDjpPvn4s7Oxn0oF09eLu7cXDy5eXhyc/HZ7UHHrlq77ohhrvfGDSj+/Us++gjXvv3+lq6asGWIiZEWruPk9rkpdhRT5iyjR1QP3AcPYt+4kQ3fvod31x7eu7E3hc4iCu2FXPlrPqOLfbj1ED5oCCFDhhAy5CQeLP+AReU/w6Ef6xxfr+hxeBxYDNp7eWa3MxkQM0DrImXRuklFW6KJCYnBarAGfeF9Yrx2/Y6qqnh8Htw+Ny6nnYqvvqLy9Xfw7TtAwXPPUfjGG9z02D+5YuoVuH1u3F43HtWD2+vWHvvcgfMDTEqbRNfwroHt1ceuvl97stT0iHR6G3qT0SWDCHMEYaawwG2YKYxoS3Rg3xsH38hNJ92EQVf/Z1J1uyl6+23yn3setbQUBbCNG0vCHXcEzYskmodO0XW4iW/bqyb/lY2MjOSVV15hxYoVXHnllZx55pk88MADhLTDPrkdUXGVi52l1UFKuvUJIUR9FJ0O1W7H3K8vsddeS+j48VrgqSfYJP6jnsk2GxB62pij71SrDI1lTE7GmJx89B2BmOuvI2ziGUFhSwtfeaAoQaGrbNHXVP5Y90s6Oh2GuDh6LF0SCJbly5bhq6zE3KMnpvRuQfPjdHZen5cyVxklzhJKnCVUuisZk1LzXr+2+TU25W+i1FlKsbOYYkcxtuxiBu1R6ZejQy2KxXNIm9ux+nL4vVuLOBij/f96wSiFr4brKe4aycLLXiPEoH0nGr/bTc+KEXXCUbQlOijAAJzT/Zwmvy5FUbRud3ojVqOVyEtnol58BeVLl1Lw4ku4c3LoOuJ09P7hslWP54gB+7Qup3Fal9Made5Lel6CbaeNaaOnBbWO1Kf29Tz18ZaWkv/U0/iqqjD37EH8XXcTOqaDTSgkTkiNDlIHDhzgb3/7G9u2bWPQoEE89thjrFu3jgcffJCTTjqJp556iqlTp7ZkWU8I3/yWjw+FPolhdIu1tXVxhBCiTamqin3tWgpefZWoSy8l7HStG0/UjBlYBg3CNnp0p+uaYhsxAtuIEfVu8zmdQY/Dp03DlJ6OJzcXd54/eOXng9eL6nAEtc4Vz3unJnTp9ZjS0jD37Im5Rw/MPXsQNmkSin8AqfbM4/NoochRQrGzWAtHjhKqXFWEU3P9yb9W/YtfDv1CibOEUmcpKjUDFph0Jtb+YS2qy4Vr7z7KFi8masdWNgxWKAn1/5i5xccFq1TAh4dDoNdj6dOH/O7RHMyI4JZThhEVmxIISFGWqEAXtWrVo6q1JkWvJ/zMMwmbMgX3/v01IUpV2XvFFZjTM4i5/jrMGRlHOVLLcu3fj6mrdl2UITaWuL/djqI3EHnRhdKaKjqMRn9SZ86cSUJCAv/5z39YvHgxN9xwA59//jkPPPAAl19+OTfccANvvPEGH3zwQUuWt9Nb7B+t78z+CW1cEiGEaDuqz0fFd99R+Mqr2DdpkzT6yisCQUofEUHoqSfeL9Y6c/BIWZEXXVhnH9XrxVNYiK+0NGi9ZeAAfJWVODMz8VVU4Nq9G9fu3ZQvXowuIoKwKVMC+xa+9jo+pyMQtExduzZbyKp9nRlAsUO7dqjYWRxoESp1llLsKMarerl7xN2Bff/4zR/5Ibv+bo8mnYn7wu4LPM6357O3bG/QPgPzQhj7u0JyIWS+PRlPVjaoKmf6t0+cdAOW004l0hxJaNpu3OZPsZ7k76o3cAA6q5X0ZqmFlqcoCqa0tMBjx6+/4tikLaWffUbY5MnE3nA9ln79WrVc7rw88p9+mtJPPiX11VcC/46jZ8xo1XII0RwaHaTWrl3Lxo0b6d69O1OmTCE9veZPSd++fVmxYgUvv/xyixTyRFHmcPPT7kJAgpQQ4sTkc7ko++ILCl97Hdfu3YB23VPEBRcQM3tWG5euY1D0egxxcVRF2cgt20+ho5AiexGFU5Mxn30Z53Y/F09uLs6dmfz4w3x0e7LxGBQ+/uVhvD4vXtXLtDe/JCy/suaYJhPFiTYOxRvJSbWxZlx8YF+Pz0O4OZxXJ78a2P/W725lc8FmvD4vHtUTtK/VYOWnGT8F9r3t+9tYn7e+3tdi1Bm5a/hdgeBVu8Un3BROlCWKCHMEUeYoIpVQDJmHqPj2W7z7D3Djb26u39cN85+uIeqUMUSYI6j6chE5r90FgAdtaGxdWBimjHTM6Rl06306IYmDtBOM7wnjp9BZhAweTLcP3qfg5Zep+OZbyhcvpnzxYmxjTyP2xhuxDh3aouf32e0UvfkmBa+8ilqlDeNdtXbtCfmDiOg8Gh2khg4dyj/+8Q+uuuoqvvnmGwYOHFhnn+uvv75ZC3eiCTMb+PTGUbz21Q90j5NufUKIE0/2LbdSsXw5oH3Bjbr8cqKv/AOGuLg2LlnbU1WVMldZYGjs6lurwcoFPWvmebz0y0vZXbIbh9dR5xg9IntwXo/zMCYmYkxM5NmCR9ibvFfb+Puu6hPhGqjSp9jGaa5uOHftQnU4iNzvInI/GLLhxT41o8DdtsCLYjZTeOB1zD17YO7Rg1JHCQX2gnpfx+Fz+URbook0R9YsFu02yhxFpCUSn+pDr2itYfeNuo9/9PsLYdYozOGRAFT+9BOH7n8AV1YWeL0c8h9XAfRATE4l0SGxAFgGDCT6qqswZWRgSu+GOSMDfUxMp+se2pCQQYNIffZZHDt2UPjyK5QtXEjlipVUrlhJ17fewjay/i6lx0P1+Sj76ivyHn8icJ2ZZfAgEu6+G+uQIc1+PiFaU6OD1Ntvv83tt9/OX/7yF0466SReeumllizXCUlRFHonhjE+ST36zkII0Ql4CgrQhYSgs2k/HkWcfx6O334j+qqriLx0euD6js7K6/NS7CymyFEUFI5sRhsX97o4sN/Fn1/MrtJdeHyeOsfoEdkjKEg5Pc5AiAoxhARGfouxxJAeEdwx7fwe51PiLEGv6NHr9BgUAwadAf0wPVZTOOm9Lkb1enFnZ7Nu1ad4d+0lMiKUx8aNwaAY0HtV4h+9DcXrIG/jfwLHvdsaAund0Z1yMqbr/oBBZ9D21+kDoajaE+OfqBNkVI8Hd1YWzt17KFn9Js49e3Dt3oNrzx68xcXo587FfNmlACghIYFh5X0mEyE9e2LOyMCckY4pPYOQkwYHjmvOSCdhzt2c6Cy9epHy2H+Iu/UWCl95FcfWrViHDwtsd+3bhzE1tUkDqjQk+/bbKV/0NQCG5CTib7+d8GnTTpjwKjq3RgeptLQ0Pvroo5YsixBCiBOEa98+Cl9/g9JPPyXuz7cSc801AIRNnkzoGWd06BHlPD4P+VX55Hhy+DHnR0rcJYFwdFmfywL7XfDZBewu3Y1P9dU5Ro/IHkFBqrpbHECYMSxoaOy08LSg5z4+/nFMehMxlpijDnF8zcBrjvp6FL0eU9eunNL1z3W2qS4X5U8+gTMzE1dmJs6dO3Hu2QtVdti6ndBuPUjxhzfV6yVzwukYu3TB1UNruTL36onOYsG5Zy/mHt0J8fd2sW/YwL4rZzZYJk9eXuC+pXdvur75BrouXViydi3TzjrrqKPICY2pa1eS/vWANpqfPzT57Hb2Xj4DQ0w0MddfT/jUqcc1+EP4lDOpXLGSmBtuIHrmlXWmERCiI2vUv4zKykpstsZ3NWvq/kIIIU4M9i1bKXz1VcqXLAGfFiDsGzcFtit6fbsdOc7j81BoL6TAXkBeVR759nxMehPn9zg/sE+dcLSs5vndI7oHBSnQJlpVUIg0RwZajaJDouka1jVovyfGPxFoXTraUNLdI7sfz8tsEsVkInzyZJg8ObBOdbtx7duHc+dODLGxgfXuAwfw5OXhycvDvr7uNVHRs2cHgpQpIwPFYsGUno45vRum9Az/dUzpmLp1C5r/S2e1Yhs1CrfbDdLKcUxqByXH77+julw4d2aSc8ed5P/3GWKuvZaIC84/6g8c3rIyCl56CVO3bkRdcgkAYVMmYx05AkNUVIu+BiHaQqOCVI8ePbjlllu4+uqrSW5gHgxVVfnmm2944oknGDt2LHPmzGnWggohhOi4KletouDll6la9XNgnW3cWGKvvZaQYcOO8MyWd3hAUhSF8anjA9uvX3I9O0t2UmgvDBpCGyAjIiMoSOkUHT7Vp014iZXkiGRirbHEWGJIDU8Neu5/T/8vIYYQIs2RDU5UWu3wLnntmWI0aq1Nh02kakxJIX3Bpzh3ZuLMrF52ojqcmLp1w9S1pn700dH0Xr+uWbqWiaaxDhlCj++/o/h/8yl66y3cBw5w6J//pOC554iePYuo6dPrTGStejwUf/ABBc88q3W9jIoiYto0dDYbiqJIiBKdVqOC1LJly7j33nu5//77Oemkkxg2bBjJyclYLBaKi4vZtm0bq1atwmg0MmfOHBl0QgghjpM7J4eyRV+jhFi0X+EzMjDEx3fY6wpKPvxQC1F6PeFnTSPmmmux9O7Voud0+9wU2gvJr8rHo3oYEl9zYfuclXPYVbKLvKo8ihxFQQEpIyIjKEgVOgoDAyfoFT0xlhjirHHEhcTV6Vb35PgnsRqthOpCWfz1YqZNa3iy0tSw1HrXd1aK0YilTx8sffocfV9FkdalNqQPCyP2Rq0rXsmHH1L4+ht4cnPJ+79HCD31VMw9e2o7qiqVK1dS+PgTuHZpg5WYMjJIuOtOFOuRu5UK0Rk0Kkj17t2bDz/8kKysLD788ENWrFjBTz/9hN1uJzY2liFDhvDKK68wbdo0dPLrkRBCHDNVVcm56y7KvloIXm/QNp3VimXQINLefCOwzp2Tgz4mps78Qm3J53BQumABtlGjMHXrBkDMtdeij4kl5uqrMKakHNfxVVWl3F1OuauclNCaYz2+9nEySzIDLUvFjuJAQEqPSOfz8z8P7LuzeCfbi7cHHusVPbEhsVo4iggOR3NPmYtBZyDOGkeUOQq9ruGuh13DtS55brf7uF6jEO2BzmrVBn65/HJKFyzAuTMzEKJUn4/kt9/m4LbfANBHRhJ7y5+Imj4dRa5REyeIJl092KVLF/7yl7/wl7/8paXKI4TopHwOR9BFxlHLV1AVFU34mFNP+O47qscTuEZBURTt13ivF+vw4ehCQ7WJU7Oy8FVV4ausDHru/muuxbV3L8aUlMBcOKb0dO1+RkbQNSotzVtaSvG771E0bx7ewkIiL7mYpH/9CwBLv34kHsPEn4v2LGJv6V4OVR3iUGXNUuWpqhOOVh9czW9FvwU9vzogxVvjg9b/5eS/4FW9xFvjiQuJI8oShU6p/3M4MK7udB9CnEh0JhNR06cHrfMcPIjt9+1gMBA9cyaxN96APjy8jUooRNs49mFYhBDiCFSXi6r166lYvoKKFSswpiTT1T9ptycvj9ivvyZn4ULyU1OJvOgiIi68AGN8/FGO2rl4iospef99iue/S+qrr2DppXV1i73pJqJnz8bSu3dgX9XlwnXgAD57zdxAqs+Ht6wMVBV3VhburCwqV6wMbDf37UvGp58EHpd+9BGm2FjMGRmYUlNRmmlkPHduLkVvvkXJ++/j80+0aUxOxtJ/QL37H6w4SHZFdlA4yq3M5VDVIcx6M+9Meyew7+tbXuf3ot/rPY7T4wx6fHX/q3F4HcSFxAW63jUUkE5NkUlAhTgevvJyiseexpA778SakdHWxRGiTUiQEkI0G3duLhUrVlC5YgWVP/4U+FIN2q+XqsulfXlXVUpHjiB68xbcBw6Q/9RT5D/zDKHjxxN5ycWEnnZaux25rTk4duygeN48Sj//AtWphYGSjz4i8e9/B8CUllbnOYrJhLl78Ghsik5Hzx9W4i0qwrV7N87de7TbvdqcO0H7+3wUPPx/qC6X9livx9SlizYxaUY61iFDCJs4scmvJfeRRyl65x3wd2XT98jAefk09o9M50dnPod+eQSv6uXvI/8eeM6fv/9znZajaiGGEFRVDVwLdnrq6fSP6U+CLYFEayKJtpolxBAS9NxpGdOaXH4hxLEx9+lDwdSpGFNPrGv9hKhNgpQQ4pipXm9Q4Mm5++6gUdn0sbGEjh1L6Nix2EafEmgBMSQkkHf++Qx9+mns335HyYcfYl+/nopvv6Xi229J/NcDgaFzOwvV56Ni+XKK582j8qdVgfWW/v2Jvmom4WeeeUzHVRQFQ0wMhpgYrMOHB59TrRlAQedyETp5Mu69e3Ht3o2vqgrXvn3aRKbff497ypRAkFJ9PvbPvgZjSjLmjAyM6d1wd0mgMi6Ucm8luZWHqPRUcU73c9CFWMDtJrtnJO8MrWRd+j7wvAg/1pQjxBDCnBFzAuGoa3hXKtwVJNoSSbIlkWBNCISjJFtS0Gv440l/PKZ6EUIIIVqaBCkhRJN4CgqoWPkDFSuWU/nTKjK++DzQJS9s/HhUu4PQcWOxjR2LpW/fI17/pAsJIfKC84m84HycmZmUfPgRZUuWBIWKihUr8DkchE2Y0KEvYFY9Hg7eex/ewkLQ6QibNInoq2YSMmRIi43EV/u4PouFhIcfotRTSqG9kMqcfdh3ZeLZsw/2Z7Olq4vsdU9x28m34cnNpernn+scz6ODCgu8cbaO7b1tnJ1xNlFXXknouHE8X/gq6w4sQ69ogzJUtx4l2ZJIsCXgVb0YFO1/OY+Ne6xFXq8QQgjRmiRICSGOSPV6cWzZErjWybFlS9D2yh9/IvKC8wGImjmT6KuuOqbzmHv0IGHO3cTfdWdQ+Mr/7zM4tmxBHxurha6LL66361t748rKpvTzz4i94QYUvR6dyUTMtdfiyc8n+ooZxzxyndvrptJdSaQlMrBu+YHlZFVkUeYso9RVSpmzjDJXGaXOUgw6Ay+f8XJg378u+ysb8jbUHDDKvwCW39Zw28m3oQsLI/nxx1jw7XO49+wluUgluRDMHoisgks3WPlmwmhcPhfmqCgMUVHcVX4X94y8h9iQ2KPOiSSEEEJ0Bk3+v123bt2YPXs2V199NV27dj36E4QQHU7ta1TKFi4i5447grZb+vfXWp1OO42QQYMC65ujZaV2iFI9HmynjMJ96BDeggIKX3mVwldexTpqFJGXXEzYpEnommnAhOagqir2desoeuttyr/9Fnw+LH36Enb6BABiZl1d7/N8qo9yVzkR5ojAupc2vcT24u2UOEsodZYGgpHdYyc+JJ5vp38b2Pf1La+zPm99vcc264OHRY+xxBBtiSbcFE64OZwIU0TQrU/1oQ8NJeKssxh/2gA8Pg8R5gjCDKHo8otw7T9Aj4x0zj5sYJAuYV2OpcqEEEKIDqvJQer222/nzTff5IEHHmDChAlcc801XHDBBZjb0RwmQoimUX0+HL/9RuWKFVSsWEnYpEnEzJ4FgO3U0egiIrCdcop2vdNpYzDExbVKuRSDgfjbbyfu1lsp//57Sj78iMoffqDq55+p+vlnwqaeSZcnn2yVshyJz+WifNEiit56G8e2bYH1ttGj0UdFBh6vPbSWPWV7AiPVHaw8GLgfExLD0ouXBvb9KeenBsNRubs86PHwxOHEWeMIN4UTYY7QQlKt+7WvlXpyQuPr6/DJZklOxpic3OjnCyGEEJ1Zk4PULbfcwi233MKmTZt4/fXXufXWW7npppuYMWMGs2fPZujQoS1RTiFEM/OWl1P5409UrFhBxcoVePMLAtsUvT4QpAzR0fT66cc2HUVPMRoJnzyZ8MmTcWdnU/LxJ5R88gkRZ50V2Md98CCVq34mfOqZ6EJCjnC05uU4mM3ei6ejFhYB4DUa2D+6Gz+PiSE30cBLQ4YE9n1mwzMNhqMCewFenzcw2ev03tOZ3G0y0ZboOq1GocbQoOf+acifjlhGmRxWCCGEaH7H3JF98ODBPP300zz22GM8//zz3HXXXbzwwgsMGDCAP//5z8yaNavFLqAWQhwf1eMh8/Qz8JXXtGwoViu20acQetpYQseeFrR/exqK3JiSQtyttxB7801B60s+/IiC558n96GHCD/nbKIuuQTLMUwAW5uqqpS5yjhYeZCDFQc5WHmQclc516RcFJjo9saNf+cSYxGRofD1yTq+PUml3LoXfHvRHdTh8XkC1wwNTRhKqCmUJFtSYCCG6ts4a1wgRAGclXFWfUUSQgghRDtxzEHK7Xbz6aef8sYbb7B06VJGjRrFNddcQ05ODvfccw/ffPMN8+fPb86yCiGayOd0Uvnjj1QsX4E7K4uur70KaF3mrCefjGvfPq273rixhAwb1q6uNzqaw8OdIT4eY5cuuLOyKHn3PUrefQ9L//5EXnIJ4WefhT40tM4x3D43uZW5FNgLOCn+pMD6x9Y8xorsFRyqPITdY9fO51M5OVPlrDUquwpfocfyZehDQ4m3JfDURSaMCYkkhCcz3h+OqgOSSk23uj8P/XPLVIYQQgghWl2Tg9T69et54403ePfdd9Hr9Vx55ZU8+eST9OnTJ7DP5MmTGTt2bLMUsLy8nPvuu49PP/2UvLw8hgwZwtNPP83w4cNxu93ce++9LFy4kN27dxMREcHEiRP5v//7P5KlH784gTm2b6fkw48o/eILfKWlgfWurGxMXbTR4lKefKJVu8C1tKjLLiVy+iVUrV5NyYcfUrb0Gxxbt3Jo61byn32Wg/Pu59eSreRU5GhLZQ55VXna4AqKnrV/WBtoOcq357OndA8AIU6Vs7aFMHGNi+hCbTJbn95B1Zo1hE2YwNxT5mI57f/QKQ0P8y6EEEKIzqfJQWr48OFMmjSJF154gfPPPx9jPfO69OvXj8suu6xZCnjttdeyZcsW5s2bR3JyMu+88w4TJ05k27ZthIaGsn79eu677z4GDx5McXExt912G+eeey5r165tlvML0ZFUrFxJ/tP/DRqi3JCYSNgZZxA69jQMcbGB9Z0hRDk8DnIqczhYcZDsimwOVh4k253NwYn5VAyO42XvDMo++gTryUN57cBiFu5ZCKrK2C0q5T0UfCEKJp2J5NBkSp2lxITEADCz/0wujDmd6A+X4/1iCWplBQC6iAiipl9C1IwZGJO0iWOtRmubvX4hhBBCtJ0mB6ndu3eTdpQ5XGw2G2+88cYxF6qa3W7n448/5rPPPgu0cM2dO5cFCxbwwgsv8OCDD7J06dKg5zzzzDOMGDGC/fv3y/DsotNTVRXV5ULnHzXT53BoIcpoJOz004m8+CJso0e3q2ucmqLKXRVoPaq+vXXIrYGWo3/89A8W7VnU4PPd06eSMWs2qt3O6JylhBpD6XkQBn85H9VkxHLGeOIvuwLbiBFB13T2j+mP2xlF5oe3g9eLKSOD6JlXEnHuueisEpyEEEIIcQxBKi8vj0OHDjFy5Mig9atXr0av1zNs2LBmK5zH48Hr9WKxWILWh4SE8MMPP9T7nNLSUhRFITIyssHjOp1OnE5n4HFZWRmgXffV1qNbVZ+/rctxIumIde7Jy6Ps888p/3QBoWdNI+YmbeAFy6mnEnv33YRNPRN9dLS2r88HPl9bFreO6rouriom35lP94jugYEW3vn9HRbuWcihqkOUOEvqPPfi7heTZNNagxJDErEarCTbkkmyJZFkSyI5NDnwOMIQgcfjAaORaWnTmJY2japffqGg9zpc27fjXLSUA4uWYuyWRujEiahuN7F/+5t2org4Yv70J0x9+2A95RQUnQ4v4O1An5NqHfEz3tFJnbc+qfPWJfXd+qTOW09j61hRa08w0ggjRozgzjvv5OKLLw5a/8knn/DII4+wevXqphzuqEaPHo3JZGL+/PkkJCTw7rvvMnPmTHr27Mn27duD9nU4HIwZM4Y+ffrwzjvvNHjMuXPncv/999dZP3/+fKzya7Nor7xebL//TsSatdi2b0fxhyNnQgL7/nIbtONRMlVVpdhXzD7vPvZ79pPlzaLYV4xDdQDwt/C/EamLBGCJfQkrnCsCz7UoFqJ0UUQqkUTqIjnNchrhunAAvKoXHbqmjxCqqpizsoj4ZQ3hGzeic7kCm/bc8TfcsbFHeLIQQgghOrOqqipmzJhBaWkp4eHhDe7X5CAVGhrKr7/+SkZGRtD6PXv2MGjQIMrLyxt45rHZtWsXs2fPZsWKFej1eoYOHUqvXr1Yv34922pNfOl2u7nkkkvYv38/y5YtO+KLrq9FKjU1lYKCgiM+rzW43W6WLl3KpEmT6r3+TDS/jlDnRS++ROn77+MtqJnryXLSSYRfeAGhU6a0u+5mHp8HBSXQyvT0hqd567e36t030hzJcxOeo290XwB2luwkpyIn0MIUZgpr0bL6KispX7SIqp9WYenfn/BLLkbfxn8HmltH+Ix3NlLnrU/qvHVJfbc+qfPWU1ZWRmxs7FGDVJO79pnNZnJzc+sEqYMHD2IwHPNo6g3q3r07y5cvp7KykrKyMpKSkrj00ktJT08P7ON2u5k+fTp79uzhu+++O2oYMpvNmP3XlNRmNBrbzQezPZXlRNGe6tzncKCYzYGWFm/uIbwFBeijo4k4/3wiL7oQc/fubVzKGpXuSjblb2JD3gY25G3g1/xfeXHiiwxN0Cbo7hfbD4POQL+YfgyNH8qA6AHs37if6WdOJ8IaEXSsfnH96Bd3fPM/NUlkJObLL4fLL2+9c7aR9vQZP1FInbc+qfPWJfXd+qTOW15j67fJyWfSpEnMmTOHzz77jIgI7QtQSUkJf//735k0aVJTD9doNpsNm81GcXExixcv5tFHHwVqQtTOnTv5/vvviYmJabEyCNGSVFXFsWULJR99TNlXX9H1jdcJGTgQgOiZMwkdO5aw8eNR2slcT7tLd/Pe7++xIW8DO4p34FODr8PalL8pEKRO73o6q7quwmLQrnd0u90s3LxQRrwTQgghRIfV5CD1+OOPM3bsWNLS0hgyZAgAGzduJCEhgXnz5jV7ARcvXoyqqvTu3ZvMzEzuuOMOevfuzaxZs/B4PFx88cWsX7+eL7/8Eq/Xy6FDhwCIjo7G1E6+cApxJJ7iYsq++JKSjz7CuWNHYH35kiWBIGXp1QtLr15tUj6f6iOzJJMNuRvoGdUzEI7KXeW8+/u7gf1SQlMYEj8ksHSPrGkxqw5QQgghhBCdRZODVEpKCr/++iv/+9//2LRpEyEhIcyaNYvLL7+8RZoZS0tLmTNnDllZWURHR3PRRRfx73//G6PRyN69e/n8888BOOmkk4Ke9/333zN+/PhmL48QzcVTXEzuv/5F+dJvUP2jwygmE2GTJxN58cVYRwxvk3LZPXa2FGwJdNPblLeJcrd27eMlvS6p6a4X3Y8/9P0Dg+MHMyRuCAm2hDYprxBCCCFEWzimi5psNhvXX399c5elXtOnT2f69On1buvWrRtNHCtDiDblq6oKDAyhDw+nat16VLcbc7++RF50ERFnn40+IuIoR2lebq8bo177EaTUWcr4D8bj8XmC9rEarAyKG0Sf6D6BdUa9kbtG3NWqZRVCCCGEaC+OeXSIbdu2sX//fly1hg0GOPfcc4+7UEJ0Jj6Xi4rvvqPko49x7txJj2+/QTEYUPR6Euf+E0N8PCH9+7dKWVRVZU/ZHjbmbWR97no25m8kJTSFlya9BECEOYJkWzIOj4MhCTXd9HpF9QpMgiuEEEIIIY4hSO3evZsLLriAzZs3oyhKoEUoMLqY19u8JRSig3Lu3EnJRx9T+tlneEtKAuvtmzZhPflkAMImTGiVsrz3+3v8lPMTG/M2UuwsDtpWaC/Ep/rQKToA5p81n3BTeNPnZhJCCCGEOIE0OUj9+c9/Jj09nW+++YaMjAx++eUXCgsLuf3223nsscdaooxCdChV69eT+8gjODb9GlhniI8n4sILiLzwQkxdu7bYuR0eB5sLNrOjeAdX9L0isP67/d+x6uAqAMx6MwNjBwZamwbHDw6EKNBapYQQQgghxJE1OUitWrWK7777jri4OHQ6HTqdjjFjxvDwww9z6623smHDhpYopxDtljsvD9XhCAQkncWihSiDgbAJ44m46CJCx4xBaYF51qrcVWzM38i63HWsPbSWzQWbcfu0gSumdJtCbEgsABf1uohTU07lpPiT6BfdL3BNlBBCCCGEODZN/mbn9XoJDQ0FIDY2lpycHHr37k1aWhrbt29v9gIK0d6oXi/2Tb9SsWI5FStW4Nz2G+Fnn03KY/8BwNKvH0n/fpDQceMwxMa2WDle+fUVnt/4PB41eGCIuJA4hiUMw+6xB9ZN6TalxcohhBBCCHEianKQGjBgAL/++isZGRmMHDmSRx99FJPJxMsvv0xGRkZLlFGINqeqKmVffknFsuVU/vAD3tLSoO21r4ECiLzoomY5b6mzlPW567UWp9y13DfqPvrHagNTJNoS8agekmxJDEsYxskJJzMscRhdw7rK9U1CCCGEEC2syUHq3nvvpbKyEoAHH3yQs88+m9NOO42YmBjef//9Zi+gEG1B9flw7duHOT0d0AZTKXrjTRzbtgGgCw8ndMyp2MaOJXTMmGZreSp3lbP64GrW5q5l7aG17CjegUrNEP9rDq0JBKkJqRP4+qKvSQlNaZZzCyGEEEKIxmtykJoypaaLUEZGBtu2baOoqIioqCj5FVx0aN7SMuy/rKZixUoqVq7EW1ZG759XobPZAIicPh33wYOEjhtLyKBBzXLNU4G9AI/PQ6ItEYAdxTv4y7K/BO3TLbxboLVpVNKowPpQUyihptDjLoMQQgghhGi6Jn0T9Hg8WCwWNm7cyIABAwLro6Ojm71gQrQG1969FC9aRJfPPmfP3++BWsP362w2nLt2ETJoEABRl1163Oc7VHmINYfWsC53Hety17G3bC+X9b6Me0bdA8DA2IH0je7LoLhBDEscxrCEYYEBI4QQQgghRPvRpCBlMBhIS0uTuaJEh+WtqERRCLQyVaxYSdHT/8Xq327q0Z3QseMIHTsW69AhKCbTcZ/T5XXxwKoHWJu7luyK7KBtCgolzpLAY5PexAfnfHDc5xRCCCGEEC3rmK6RmjNnDu+88460RIl2T1VVXLt3U7F8BRUrVlC1bh2J/7iPqEsuASB03FjKf/yBPVHRjLjxBqzduh3XufaV7WNt7loq3ZVc1f8qQAtHaw6tIacyB72ip29030Br05CEIYSbwpvjpQohhBBCiFbU5CD13//+l8zMTJKTk0lLS8Pm/2W/2vr165utcEIcC5/LReVPP1G5YgUVy1fgzg5uBXJs3Qr+IGVKSyP52WfZuHAhxpRjG7ShwF7AV7u/YkHmAjJLMgEIM4bxh75/QK/TA/DXYX/FZrQxJH4INqPtSIcTQgghhBAdQJOD1Pnnn98CxRDi+HgrKtGHagFFtdvJuulm8PkAUEwmrCNGEDp2LKFjT8N0HK1Otf188Gfm/zaflVkrA3M5GXVG7fqmhGE4vU6sOq3ToMzjJIQQQgjRuTQ5SP3zn/9siXII0STeigrsGzdRsWI5lctXoI+Jodv8/wGgj4gg/Mwz0UWEEzp2LLaRI9FZrUc5YuOoqhoYnXJd7jq+P/A9AINiB3Fej/M4M/1M6aonhBBCCHECOP7xm4VoJUVvvUXFDz/izMzEc/Bg0Dbl0CF8lZWBQSRSnni82c5b7ipn0Z5FLMhcwDUDr+GMrmcAcF7383B4HJzf43y6R3ZvtvMJIYQQQoj2r8lBSqfTHXG+KBnRTxwLn8OBa/dunDt34szMxLkzE9f+/WR88TmKXrvOyP7rZipXrgw8x5CUhO3U0Vqr0+jRgRDVLOVRffxy6BcWZC7gm33f4PQ6AViQuSAQpLqEdeH2Ybc32zmFEEIIIUTH0eQg9emnnwY9drvdbNiwgbfeeov777+/2QomOiefy4ViNAbCeMErr1Dy0Ue4D2QFrmmqzZ2VhSktDYCICy7AOnw45p49MPfogT4iotnL5/V5eenXl/gs8zNyKnMC63tE9uD8HudzdsbZzX5OIYQQQgjR8TQ5SJ133nl11l188cX079+f999/n2uuuaZZCiY6NtXtxrV/v9bCtDPT38q0E9e+ffT49huMiYkA+KqqcO/bD4A+MhJzjx6YevbA3LMn5h49MCQkBI4ZOubUFimrx+cJ3Nfr9CzPWk5OZQ5hxjCmZUzj/B7n0z+m/xFbYoUQQgghxIml2a6RGjlyJNddd11zHU50EKrXi2v/foxJSegsFgAKX3udvKeeAre73uc4d2YGglTEuediGzFCa2GKjW21sKKqKr8W/MqCzAV8v/97/mj+Y2DbHwf/kSp3Fad3PR2LwdIq5RFCCCGEEB1LswQpu93OM888Q5cuXZrjcKIdUn0+3NnZWutS9XVMmZm4du1Cdbno+tZb2EaOAEAfHQ1uNzqrFVMPrRtedQuTuWdwK5M5PR1zenqrvY4CewFf7PqCBZkL2F26O7B+m7KNC7kQgPGp41utPEIIIYQQomNqcpCKiooKajVQVZXy8nKsVivvvPNOsxZOtB8lH37EoQaGvlcsFjz5+YHHYWecjvWbbzAmJ6HodK1VxCPaU7qHJ9Y+wcrslXhVbUAUi97CpLRJnJ1+Nrlrc9u4hEIIIYQQoiNpcpB68skng4KUTqcjLi6OkSNHEhUV1ayFE+2HuWcPFKMRU/fuWstSjx6Ye2mtTMaUlMDIegD68HD04W0/l1KVuwqrUZs/yma0sSJ7BT7Vx+C4wZzf43zO7HYmoaZQ3G43C5WFbVxaIYQQQgjRkTQ5SP1/e/cdHkW1/w/8Pdlskk2lpGxCSUIJJSAJTUJJoYQmReEGBDGBL6IiIiBIkRKagCIiIES9FK80pcjlCgIBEzoKkqUIUoQEBGIoIb1sduf3B7+sLClkU2Y2yfv1PPuYPXvmzGc+OebZDzNzJiIiogLCIHOUeTYOKn8/CIIAVatWaBJ3FoKleT96LCUnBT/d/Ak/XP8BTlZO+Cr0KwCAq60rIgMi0cq1FRo4NZA5SiIiIiKq7Ez+Vrx+/XrY29vjX//6l1H7tm3bkJmZifDw8HILjuTzePt23Js5CzWHD4fbzA+NzjiZG51eh1/u/YJd13fh0K1DyNXnAgCsLKzwOPsxatjUAAC83PhlGaMkIiIioqrE5EJq8eLFiIqKKtDu6uqKMWPGsJCqAlKjo3Fv9pP7oSxUNma97Peu67vwheYLJGYkGtp8avpgYKOB6Nugr6GIIiIiIiIqTyYXUgkJCfAuZJU1T09P3Lp1q1yCIvlknPoFdye9D+j1cBo8CC7vvy93SMUSRRGJGYlwtHJEH+8+eLnxy2hWq5lZF39EREREVPmZXEi5urri/Pnz8PLyMmo/d+4cateuXV5xkQyyLlzEX2PHQtRq4dCjO9wjI82qINGLemy7sg3OKmd08+wGAOjp1RMqSxVC6ofAWmEtc4REREREVF2YXEgNHToU48ePh4ODAwIDAwEAhw8fxnvvvYehQ4eWe4AkjZwbN3F7zBjoMzNh26EDPJYuNauFJa4lX8O8k/Ogua9BbZvaaOfeDo5WjrBV2qKXdy+5wyMiIiKiasbkb8oLFixAQkICunXrBsv//0Vbr9fj9ddfx0cffVTuAZI0sn//HbqUFNi0aIG6q1bBwto8zu5k52Xjq/NfYf3F9cgT82BraYsxL4yBnaWd3KERERERUTVmciFlZWWF7777DgsWLIBGo4FKpULLli3h6elZEfGRRJz6vQSFowNsWraEwt48ipRf7v2CeSfn4Vbak3vvQuqFYMaLM6C2U8scGRERERFVd6W+dqtx48Zo3LhxecZCEtNnZECfkwPLWrUAAPZBQTJH9I8/H/+J0QdGAwBcVa6Y8eIMw31RRERERERyszB1g8GDB2Px4sUF2j/55JMCz5Yi86XPzcVf745HwrDh0N65I3c4BTSs0RADGg7A0CZDsWvgLhZRRERERGRWTC6kDh8+jL59+xZo79WrF44cOVIuQVHFEnU63P1gKjJOnIA2KQl5jx7JHRJup97G+J/HGz0Pal6nefiww4dwsHKQMTIiIiIiooJMvrQvPT0dVlZWBdqVSiVSU1PLJSiqOKIoInHuPKTt2wcolai3aiVULVvKFo9Wr8U3v3+DqHNRyNHlwNLCEsuClwEALAST63wiIiIiIkmY/E21RYsW+O677wq0b926Fc2bNy+XoKji3F/+OR5//z0gCKjzySew69hRtljO3T+HIT8OwednP0eOLgcd3DtgYuuJssVDRERERFRSJp+RmjVrFgYNGoQ///wTXbt2BQAcOnQIW7ZswbZt28o9QCo/D9dvwMMvvwQAqCMj4dirpyxxpOWm4fOzn+P7K99DhIia1jUxpd0UvNTgJbN6ADARERERUVFMLqT69++PXbt24aOPPsL27duhUqnwwgsv4ODBgwgyo1XfyJg+KwvJW7cAAFwmTkTNIWGyxbLp8iZ8d+XJWc0BDQfg/bbvo6ZNTdniISIiIiIyVamWP+/bt2+hC05oNBr4+fmVNSaqABYqFbw2bkTq3r2o+frrku9fFEXD2aYI3who7msw0nckXnR/UfJYiIiIiIjKqsx386ekpGD16tVo3bo12rRpUx4xUTnSpaUZfrZ0cUGt8HBJL5/T6XXYdHkT3oh+Azq9DgBgY2mDqO5RLKKIiIiIqNIqdSH1888/Y/jw4XB3d8fKlSvRp08fnDlzpjxjozLKvnwZf3bvgcc/7JJl/1ceXcFre1/D4l8X45d7v2B//H5Z4iAiIiIiKm8mXdr3119/YcOGDVi3bh0yMjIQFhYGrVaLHTt2cMU+M5MbH49bo9+ALiUFKTt3wmlAfwgW0iwnnpWXhTWaNfjPpf9AJ+rgoHTAhDYT0Mu7lyT7JyIiIiKqaCX+Zt2nTx80b94cly5dwsqVK3H37l2sXLmyImOjUtL+nYRb/zcauocPYd2sGequ/kKyIur4neN4+b8vY/3v66ETdQj1DMV/B/4XYU3C+FwoIiIiIqoySvzN9sCBAxg9ejTmzp2Lvn37QqFQVGRcBmlpaZgwYQI8PT2hUqnQsWNHnD592vC5KIqIjIyEh4cHVCoVgoOD8fvvv0sSmznSpaTg9ujR0N65A6VnfdT/+isoHBwk2bde1GNV3CrcSb8DtZ0aK7uuxKfBn8LF1kWS/RMRERERSaXEhdTRo0eRlpaGtm3b4sUXX8SqVatw//79iowNADB69GhER0fj22+/xYULFxAaGoru3bvjzp07AICPP/4Yy5Ytw6pVq3D69Gmo1Wr06NEDaU8tslBd6DMzcfutt5Fz7RosXV1Rf+06WDo7V+g+RVGEVq8FAFgIFpjTcQ5ea/Ya/jvgvwiuF1yh+yYiIiIikkuJ75EKCAhAQEAAPv/8c2zduhXr1q3DpEmToNfrER0djXr16sGhnM98ZGVlYceOHfjvf/+LwMBAAEBkZCR27dqFNWvWYP78+Vi+fDk+/PBDvPLKKwCAb775Bm5ubti8eTPefPPNQsfNyclBTk6O4X1qaioAQKvVQqvVlusxmCp//6WJI2XnTmTFxcHC0RHuUWsguLlW6PHEp8Zj4a8L8YLzC3jX710AQEOHhpjkPwlA6Y5BDmXJOZUOcy4t5lt6zLn0mHNpMd/SY86lU9IcC6IoiqXdyZUrV7B27Vp8++23ePz4MXr06IHdu3eXdrgC0tLS4OjoiIMHD6Jbt26G9oCAAFhbW2PdunVo2LAhzp49C39/f8PnAwYMQI0aNfDNN98UOm5kZCTmzp1boH3z5s2wtbUtt/glJ4qodegQMhs1RraXZ4XtJk/Mw5GcIzicfRg66GADG0x2mgwbwabC9klEREREJIXMzEwMGzYMKSkpcHR0LLJfmQqpfDqdDv/73/+wbt26ci2kAKBjx46wsrLC5s2b4ebmhi1btuD1119H48aNsX79enTq1Al37tyBh4eHYZsxY8YgISEB+/cXvtx2YWek6tWrhwcPHhSbLClotVpER0ejR48eUCqVz+0viiKg00GwLNWzlU12NuksFvy6APGp8QCATu6dML3ddHjYexS/oRkzNedUdsy5tJhv6THn0mPOpcV8S485l05qaiqcnZ2fW0iVy7dvhUKBgQMHYuDAgeUxnJFvv/0Wo0aNQp06daBQKNC6dWsMGzYMZ8+eNfR59gGzoigW+9BZa2trWFtbF2hXKpVmMzFLGsv9lauQffEi6iz/DBYqVYXFk5KTgs9++ww7ru0AANSyqYXp7aejp1dPSR/wW5HM6fdfXTDn0mK+pcecS485lxbzLT3mvOKVNL9mvx51w4YNcfjwYaSnp+P27dv49ddfodVq4e3tDbVaDQBITEw02iYpKQlubm5yhCupR99uxIMvvkD64cNIP3ykQveVqc3E3pt7AQCDGg/C7oG70cu7V5UpooiIiIiITCHN9WDlwM7ODnZ2dkhOTsb+/fvx8ccfG4qp6Ohowz1Subm5OHz4MJYsWSJzxBUr5X//w98LFwIAnN8dB8dePSt0f+727pjVYRbc7dzRVt22QvdFRERERGTuzL6Q2r9/P0RRRJMmTXD9+nVMmTIFTZo0wciRIyEIAiZMmICPPvoIjRs3RuPGjfHRRx/B1tYWw4YNkzv0CpN++DDuTp8BAKj52mtwHjtWkv32a9hPkv0QEREREZk7sy+kUlJSMH36dPz111+oVasWBg0ahIULFxquXfzggw+QlZWFsWPHIjk5GS+++CIOHDhQ7kuxm4vMs2fx13sTgLw8OL70EtxmTK/Qy+vikuJw/v55BHgEwKemT4Xth4iIiIioMjH7QiosLAxhYWFFfi4IAiIjIxEZGSldUDLR5+bizvuTIWZnwy6wCzwWfQTBomJvc9t3cx82/7EZrzZ9FTNenFGh+yIiIiIiqizMfrEJ+oeFlRXqrvgc9t26oe7nn0OQYMWWuKQ4AIC/q/9zehIRERERVR9mf0aKjKlatkS9L1ZJsq9MbSauJl8FwEKKiIiIiOhpPCNl5nSpqUgYORJZFy5Ivu/zD85DJ+rgbucOtZ1a8v0TEREREZkrFlJmTJ+Vhdtvj0XmyVO4M3kyxLw8Sfeff1mfn6ufpPslIiIiIjJ3LKTMlKjV4s7EScj67TdYODg8uSfKUtorMTVJGgC8rI+IiIiI6FkspMyRXo+k2XOQHhsLwdoa9aLWwKZpU0lDEEURlx9eBgC0dm0t6b6JiIiIiMwdF5swM6IowuXHPUg7fhxQKFDn8+WwbdNG8jgEQcCBwQfw+8Pf0ahGI8n3T0RERERkzlhImZm03btR8/hxAIDHoo/gEBwsWyw2ljZo4yZ9EUdEREREZO54aZ+ZsQ8NRXrTpnCeNhVO/fvLHQ4RERERERWChZSZsVCpcDf8ddQYPlzWON4++DYWnlqIh1kPZY2DiIiIiMgcsZAyRxby/loSMxJx7M4xbLu6DSpLlayxEBERERGZIxZSVED+86Oa1GoCW6WtzNEQEREREZkfFlJUwNm/zwLg86OIiIiIiIrCQooK0NzXAAD8XP1kjYOIiIiIyFyxkCIjGdoMXE2+CgDwd+EZKSIiIiKiwrCQIiPn7p+DXtSjjn0duNm5yR0OEREREZFZYiFFRtJz0+Fu587L+oiIiIiIimEpdwBkXkK9QhHqFYpcXa7coRARERERmS2ekaJCWSms5A6BiIiIiMhssZAig1xdLvSiXu4wiIiIiIjMHgspMth2dRu6bO2CLzRfyB0KEREREZFZYyFFBpokDVJzU2Ep8NY5IiIiIqLisJAiAIAoijibdBYA4O/K50cRERERERWHhRQBAO5l3ENSZhIUggItnFvIHQ4RERERkVljIUUAgLikOABAs1rNYKu0lTkaIiIiIiLzxkKKAPxTSPFBvEREREREz8dCigD8U0jx/igiIiIioufj8mwEURQRUi8EjlaOLKSIiIiIiEqAhRRBEASM8x8ndxhERERERJUGL+0jIiIiIiIyEQspgiZJg5ScFLnDICIiIiKqNFhIVXNavRZjoseg89bOuJlyU+5wiIiIiIgqBRZS1dzVR1eRlZcFBysHeDp6yh0OEREREVGlwEKqmjM8P8rFDxYCpwMRERERUUnwm3M1x+dHERERERGZjoVUNSaK4j9npFz95A2GiIiIiKgSYSFVjd1Jv4P7WfdhKViihXMLucMhIiIiIqo0WEhVY/lno5rVbgaVpUrmaIiIiIiIKg9LuQMg+bRTt0NkQCRslbZyh0JEREREVKmwkKrG1HZqDPIZJHcYRERERESVDi/tIyIiIiIiMpFZF1J5eXmYOXMmvL29oVKp0KBBA8ybNw96vd7QJz09HePGjUPdunWhUqnQrFkzrFmzRsaoK4fLDy9j0+VNuJ58Xe5QiIiIiIgqHbO+tG/JkiWIiorCN998A19fX5w5cwYjR46Ek5MT3nvvPQDAxIkTERMTg40bN8LLywsHDhzA2LFj4eHhgQEDBsh8BOYrOiEaX1/4Gv0b9sfCzgvlDoeIiIiIqFIx6zNSJ0+exIABA9C3b194eXlh8ODBCA0NxZkzZ4z6hIeHIzg4GF5eXhgzZgxatWpl1IcK0tzXAOCDeImIiIiISsOsz0h17twZUVFRuHr1Knx8fHDu3DkcO3YMy5cvN+qze/dujBo1Ch4eHoiNjcXVq1fx+eefFzluTk4OcnJyDO9TU1MBAFqtFlqttsKOpyTy91+RcWj1Wly4fwEA0LJWS9mPWW5S5JyMMefSYr6lx5xLjzmXFvMtPeZcOiXNsSCKoljBsZSaKIqYMWMGlixZAoVCAZ1Oh4ULF2L69OmGPrm5uXjjjTfwn//8B5aWlrCwsMC///1vjBgxoshxIyMjMXfu3ALtmzdvhq1t1V8K/K+8vxCVHgWVoMJ0x+mwEMz6xCQRERERkWQyMzMxbNgwpKSkwNHRsch+Zn1G6rvvvsPGjRuxefNm+Pr6QqPRYMKECfDw8EB4eDgAYMWKFTh16hR2794NT09PHDlyBGPHjoW7uzu6d+9e6LjTp0/HpEmTDO9TU1NRr149hIaGFpssKWi1WkRHR6NHjx5QKpUVso+Nf2wEzgJt3dvipeCXKmQflYkUOSdjzLm0mG/pMefSY86lxXxLjzmXTv7Vas9j1oXUlClTMG3aNAwdOhQA0LJlSyQkJGDRokUIDw9HVlYWZsyYgR9++AF9+/YFALzwwgvQaDRYunRpkYWUtbU1rK2tC7QrlUqzmZgVGcuFh08u62utbm02x2sOzOn3X10w59JivqXHnEuPOZcW8y095rzilTS/Zn1NV2ZmJiwsjENUKBSG5c/z72kqrg8VdP7+eQBcaIKIiIiIqLTM+oxUv379sHDhQtSvXx++vr6Ii4vDsmXLMGrUKACAo6MjgoKCMGXKFKhUKnh6euLw4cP4z3/+g2XLlskcvfnaPXA3Ljy4gBbOLeQOhYiIiIioUjLrQmrlypWYNWsWxo4di6SkJHh4eODNN9/E7NmzDX22bt2K6dOnY/jw4Xj06BE8PT2xcOFCvPXWWzJGbt5slbZ40f1FucMgIiIiIqq0zLqQcnBwwPLly42WO3+WWq3G+vXrpQuKiIiIiIiqPbMupKj8TT0yFbVVtRHhGwFXW1e5wyEiIiIiqpTMerEJKl8pOSnYe3Mvvr30LSwtWEMTEREREZUWC6lq5Nz9cwAAL0cv1LKpJXM0RERERESVFwupaiQuKQ4A4OfqJ28gRERERESVHAupaiS/kOLzo4iIiIiIyoaFVDWh1Wlx8cFFACykiIiIiIjKioVUNXHp0SXk6HJQ07omvBy95A6HiIiIiKhS49Jt1URydjJcVC7wdfaFIAhyh0NEREREVKmxkKomgusFI6huELLysuQOhYiIiIio0uOlfdWIIAiwVdrKHQYRERERUaXHQqoayNPnQRRFucMgIiIiIqoyWEhVAz/e+BHB3wdjxdkVcodCRERERFQlsJCqBjRJGjzKfoQ8MU/uUIiIiIiIqgQWUtXA2aSzAAB/Fz4/ioiIiIioPLCQquIeZz/GzZSbAAA/Vz95gyEiIiIiqiJYSFVxmvsaAIC3kzdq2tSUNxgiIiIioiqChVQVF5cUBwDwd+VlfURERERE5YWFVBWnSdIAAPxc/GSNg4iIiIioKrGUOwCqWO3d20Mv6tHarbXcoRARERERVRkspKq4d/zewTt+78gdBhERERFRlcJL+4iIiIiIiEzEQqoK++PRH0jPTZc7DCIiIiKiKoeFVBUliiLGHBiDTls74Y9Hf8gdDhERERFRlcJCqoqKT41Hck4yLAVLNHBqIHc4RERERERVCgupKip/2fMWzi1gpbCSNxgiIiIioiqGhVQVxQfxEhERERFVHBZSVRQLKSIiIiKiisNCqgp6lP0I8anxAAA/Vz9ZYyEiIiIiqopYSFVB+fdHNXRqCCdrJ3mDISIiIiKqgizlDoDKn29tX8zqMAtKC6XcoRARERERVUkspKogNzs3hDUJkzsMIiIionKh0+mg1WrlDkNWWq0WlpaWyM7Ohk6nkzucSk2pVEKhUJR5HBZSRERERGSWRFFEYmIiHj9+LHcoshNFEWq1Grdv34YgCHKHU+nVqFEDarW6TLlkIVXF3Ei5gTOJZ9DWrS0a1OCDeImIiKjyyi+iXF1dYWtrW60LCL1ej/T0dNjb28PCgssclJYoisjMzERSUhIAwN3dvdRjsZCqYmJuxWD52eXoXr87Pgv5TO5wiIiIiEpFp9MZiqjatWvLHY7s9Ho9cnNzYWNjw0KqjFQqFQAgKSkJrq6upb7Mj7+FKiZ/xT4ue05ERESVWf49Uba2tjJHQlVR/rwqy713LKSqEL2oR9x9PoiXiIiIqo7qfDkfVZzymFcspKqQ+JR4pOSkwFphjWa1mskdDhERERFRlcVCqgqJS3pyNqqlc0soFXyGFBERERFRRWEhVYXkF1K8rI+IiIiITBUYGIjNmzdX6D4EQcCuXbtK3P/HH3+Ev78/9Hp9xQVVSiykqpBz988B4EITRERERHKKiIiAIAhYvHixUfuuXbvKfG/Ohg0bUKNGjSI/P3LkCPr16wcPDw+TipYff/wRiYmJGDp0KGJjYyEIQrGvDRs2lCr+e/fuoXfv3iXu/9JLL0EQhAov8EqDhVQVsrnvZkR1j0IbtzZyh0JERERUrdnY2GDJkiVITk6WdL8ZGRlo1aoVVq1aZdJ2K1aswMiRI2FhYYGOHTvi3r17hldYWBh69epl1DZkyBDDtjqdrsRnjNRqNaytrU2KbeTIkVi5cqVJ20jBrAupvLw8zJw5E97e3lCpVGjQoAHmzZtX4Bd1+fJl9O/fH05OTnBwcECHDh1w69YtmaKWj4OVAzrV6QQ7pZ3coRARERGVO1EUkZmbJ8tLFEWTYu3evTvUajUWLVpUbL8dO3bA19cX1tbW8PLywqefflqWFKF3795YsGABXnnllRJv8+DBAxw8eBD9+/cHAFhZWUGtVhteKpUK1tbWhvf79u2Du7s7fvzxRzRv3hzW1tZISEjA6dOn0aNHDzg7O8PJyQlBQUE4e/as0b6ePksWHx8PQRCwc+dOhISEwNbWFq1atcLJkyeNtunfvz9+/fVX3Lhxo0y5KW9m/UDeJUuWICoqCt988w18fX1x5swZjBw5Ek5OTnjvvfcAAH/++Sc6d+6M//u//8PcuXPh5OSEy5cvw8bGRuboiYiIiKg8ZWl1aD57vyz7vjSvJ2ytSv7VWaFQ4KOPPsKwYcMwfvx41K1bt0Cf3377DWFhYYiMjMSQIUNw4sQJjB07FrVr10ZEREQ5Rl+8Y8eOwdbWFs2alXzV58zMTCxatAj//ve/Ubt2bbi6uuLmzZsIDw/HihUrAACffvop+vTpg2vXrsHBwaHIsT788EMsXboUjRs3xocffohXX30V169fh6Xlk3x7enrC1dUVR48eRYMGDcp2sOXIrAupkydPYsCAAejbty8AwMvLC1u2bMGZM2cMfT788EP06dMHH3/8saHNnBIslfkn58POyg7Dmg6D2k4tdzhERERE1d7LL78MPz8/zJkzB2vXri3w+bJly9CtWzfMmjULAODj44NLly7hk08+kbSQio+Ph5ubGywsSn6xmlarxerVq9GqVStDW9euXY36fPnll6hZsyYOHz6Ml156qcixJk+ebPi+P3fuXPj6+uL69eto2rSpoU+dOnUQHx9f4vikYNaFVOfOnREVFYWrV6/Cx8cH586dw7Fjx7B8+XIAgF6vx549e/DBBx+gZ8+eiIuLg7e3N6ZPn46BAwcWOW5OTg5ycnIM71NTUwE8mRBlebpxecjfvylxZOdlY+f1ncjT5+HlBi9DayXvMVQ2pck5lQ1zLi3mW3rMufSYc2lJkW+tVgtRFKHX6w23dVgrBFyM7FFh+yyOtUIo8X1AoigaYl+0aBG6d++OiRMnGrbP/2/+7SlPjxsQEIDly5dDq9VCoVAYjfm0ksbydP6KkpmZCRsbmyL7PX08+WNaWVmhRYsWRtskJSVhzpw5iImJwd9//w2dTofMzEwkJCQY9cuPKb/t6XHc3NwAAImJifDx8TFso1KpkJGRUW6r9+n1eoiiWCDPQMnntVkXUlOnTkVKSgqaNm0KhUIBnU6HhQsX4tVXXwXw5JeVnp6OxYsXY8GCBViyZAn27duHV155BTExMQgKCip03EWLFmHu3LkF2g8cOABbW9sKPaaSio6OLnHfm3k3kafPg71gj3OHz+G8cL4CI6u6TMk5lQ/mXFrMt/SYc+kx59KqyHxbWlpCrVYjPT0dubm5FbafkkrLLnlfrVaLvLw8pKamws/PD127dsXUqVMxbNgwAP/8I35eXh5yc3MN74EnRU1+n2e/4ANAdnY2RFE02qY4WVlZz+1rZ2eHR48eFdnv6ePJj8HGxgZpaWlG/UaMGIGHDx9iwYIFqFevHqytrREaGorU1FSjsfNjSk9PBwCjHOS3paenG23z4MEDODg4lPi4nyc3NxdZWVk4cuQI8vLyjD7L/x08j1kXUt999x02btyIzZs3w9fXFxqNBhMmTICHhwfCw8MNFemAAQMwceJEAICfnx9OnDiBqKioIgup6dOnY9KkSYb3qampqFevHkJDQ+Ho6FjxB1YMrVaL6Oho9OjRA0plyR6qu+73dcA54MW6L6Jvl74VHGHVU5qcU9kw59JivqXHnEuPOZeWFPnOzs7G7du3YW9vX+nufVcqlbC0tDR8r/zkk0/QunVrNG/eHAAM7S1atMDp06eNvn9qNBr4+PigZs2aRmOKooi0tDTY2NhAEIQSf2dVqVTP7RsQEGA4g/Tsfgs7nqJiOHXqFFatWoXBgwcDAG7fvo2HDx/CxsbGqG9+TPb29gCeFHL5n+d/v7e1tTW0ZWdn4+bNm+jQoUO5fVfPzs6GSqVCYGBggflV0mLNrAupKVOmYNq0aRg6dCgAoGXLlkhISMCiRYsQHh4OZ2dnWFpaGiZlvmbNmuHYsWNFjmttbV3osotKpdJs/viaEsv5h0/OQLVRtzGb+Csjc/r9VxfMubSYb+kx59JjzqVVkfnW6XQQBAEWFhYm3btjDvKftZQfd6tWrTB8+HDDkuT57ZMnT0a7du2wcOFCDBkyBCdPnsQXX3yB1atXFzjmpy9p0+l0OH/e+AokKysrNG/eHOnp6bh+/bqhPSEhAefPn0etWrVQv379QuNt06YNXFxccPLkyULvZXr2eJ79b75GjRph06ZNaN++PVJTUzFlyhSoVCqjbfO3e/r3+uzPz7b9+uuvsLa2RqdOncptLlhYWEAQhELncEnntFnPyszMzALJUigUholkZWWFdu3a4cqVK0Z9rl69Ck9PT8nilJNe1CMuKQ4A4O/qL3M0RERERFSY+fPnF7jPqXXr1vj++++xdetWtGjRArNnz8a8efOeu9BEeno6/P39jV59+vQBAJw5c8bQBgCTJk2Cv78/Zs+eXeR4CoUCo0aNwqZNm8p0jOvWrUNycjL8/f0xYsQIjB8/Hq6urmUaEwC2bNmC4cOHm80tOPnM+oxUv379sHDhQtSvXx++vr6Ii4vDsmXLMGrUKEOfKVOmYMiQIQgMDERISAj27duH//3vf4iNjZUvcAndeHwDablpUFmq0KRWE7nDISIiIqr2NmzYUKDN09MT2dkFb7QaNGgQBg0aVOKxIyIijL4LPys4ONjkZ14BwIQJE+Dr64uEhIQCJySePZ6IiIhCiz1/f3+cPn3aqC3/Mr98T8fm5eVVINYaNWoYtd2/fx/bt283WrXbXJh1IbVy5UrMmjULY8eORVJSEjw8PPDmm28aVdQvv/wyoqKisGjRIowfPx5NmjTBjh070LlzZxkjl87fmX+jpnVNNK7ZGEoLXspARERERKZzc3PD2rVrcevWLbO6suvmzZtYvXo1vL295Q6lALMupBwcHLB8+XLDcudFGTVqVLGVeVXWqU4nHB5yGGnatOd3JiIiIiIqwoABA+QOoYD27dujffv2codRKLO+R4pKRhAEOFrJu9ogEREREVF1wkKqEtOL+lJdA0tERERERGXDQqoSO3TrELpv647PfvtM7lCIiIiIiKoVFlKVWFxSHJKykpChzZA7FCIiIiKiaoWFVCWmSdIAAPxc/WSNg4iIiIioumEhVUll5WXh8sPLAPggXiIiIiIiqbGQqqQuPriIPDEPripXeNh5yB0OEREREVVygYGB2Lx5syz7njx5MsaPHy/LvkuLhVQlFZcUBwDwd/OHIAgyR0NERERE+SIiIiAIAhYvXmzUvmvXrjJ/b9uwYQNq1KhR5OdHjhxBv3794OHhAUEQsGvXrhKN++OPPyIxMRFDhw5FbGwsBEEo9rVhw4ZSxR8fHw9BEKDRaIzaP/jgA6xfvx43b94s1bhyYCFVSRkKKV7WR0RERGR2bGxssGTJEiQnJ0u634yMDLRq1QqrVq0yabsVK1Zg5MiRsLCwQMeOHXHv3j3DKywsDL169TJqGzJkSLnG7erqitDQUERFRZXruBWJhVQl1dK5JVo6t0Rr19Zyh0JEREQkDVEEcjPkeZn47M7u3btDrVZj0aJFxfbbsWMHfH19YW1tDS8vL3z66adlyRB69+6NBQsW4JVXXinxNg8ePMDBgwfRv39/AICVlRXUarXhpVKpYG1tbXjv5uaGlStXokGDBlCpVGjVqhW2b99uGC85ORnDhw+Hi4sLVCoVGjdujPXr1wMAvL29AQD+/k+uqgoODjZs179/f2zZsqVMxy8lS7kDoNIZ6zcWY/3Gyh0GERERkXS0mcBHMt0bPuMuYGVX4u4KhQIfffQRhg0bhvHjx6Nu3boF+vz2228ICwtDZGQkhgwZghMnTmDs2LGoXbs2IiIiyjH44h07dgy2trZo1qxZifrPnDkTO3fuxJo1a9C4cWMcOXIEr732GlxcXBAUFIRZs2bh0qVL+Omnn+Ds7Izr168jKysLAPDrr7+iffv2OHjwIHx9fWFlZWUYt3379rh9+zYSEhLg6elZIcdanlhIERERERFVgJdffhl+fn6YM2cO1q5dW+DzZcuWoVu3bpg1axYAwMfHB5cuXcInn3wiaSEVHx8PNzc3WFg8/2K1jIwMLFu2DD///DMCAgIAAA0aNMCxY8fw5ZdfIigoCLdu3YK/vz/atm0LAPDy8jJs7+LiAgCoXbs21Gq10dh16tQxxMNCiirEjcc3oLZTw1ZpK3coRERERNJR2j45MyTXvkthyZIl6Nq1K95///0Cn12+fBkDBgwwauvUqROWL18OnU4HhUJRqn2aKisrCzY2NiXqe+nSJWRnZ6NHjx5G7bm5ufD3f3Lv/ttvv41Bgwbh7NmzCA0NxcCBA9GxY8fnjq1SqQAAmZmZJh6BPFhIVUJjD41FYkYiNvTawIfxEhERUfUhCCZdXmcOAgMD0bNnT8yYMaPAWSZRFAus4ieaeC9WeXB2di7xohh6vR4AsGfPHsMZpHzW1tYAntynlZCQgD179uDgwYPo1q0b3nnnHSxdurTYsR89egTgn7NW5o6FVCWTlJmEO+l3YCFYoFGNRnKHQ0RERETPsXjxYvj5+cHHx8eovXnz5jh27JhR24kTJ+Dj4yPZ2SjgycIPiYmJSE5ORs2aNYvt27x5c1hbW+PWrVsICgoqsp+LiwsiIiIQERGBLl26YMqUKVi6dKnhniidTldgm4sXL0KpVMLX17dsByQRFlKVTP6y5z41fWBvZS9zNERERET0PC1btsTw4cOxcuVKo/b3338f7dq1w/z58zFkyBCcPHkSq1atwurVq4sdT6fTFXgOk5WVFZo3b4709HRcv37d0H7z5k1oNBrUqlUL9evXL3Q8f39/uLi44Pjx43jppZeK3beDgwMmT56MiRMnQq/Xo3PnzkhNTcWJEydgb2+P8PBwzJ49G23atIGvry9ycnLw448/GhaycHV1hUqlwr59+1C3bl3Y2NjAyckJAHD06FF06dLFcImfuePy55WMJkkDAPBz8ZM1DiIiIiIqufnz5xe4bK9169b4/vvvsXXrVrRo0QKzZ8/GvHnznrvQRHp6Ovz9/Y1effr0AQCcOXPG0AYAkyZNgr+/P2bPnl3keAqFAqNGjcKmTZtKfCyzZ8/GokWL0KxZM/Ts2RP/+9//DEubW1lZYfr06XjhhRcQGBgIhUKBrVu3AgAsLS2xYsUKfPnll/Dw8DC6R2zLli144403ShSDOeAZqUqGD+IlIiIiMm8bNmwo0Obp6Yns7OwC7YMGDcKgQYNKPHZERARGjRpV5OfBwcGlus9qwoQJ8PX1LXTp8WePRxAEjB8/HuPHjy90rJkzZ2LmzJlF7mv06NEYPXq0UduePXugUCgwePBgk2OXC89IVSKZ2kz88egPACykiIiIiKj8uLm5Ye3atbh165Ys+8/IyMD69ethaVl5zvNUnkgJFx5cgE7Uwc3WDe727nKHQ0RERERVyLNLsUspLCxMtn2XFgupSsTL0QvT2k+TOwwiIiIiomqPhVQl4mbnhuHNhssdBhERERFRtcd7pIiIiIiIiEzEQqqSuJt+Fzuv7URCaoLcoRARERERVXsspCqJo38dxZwTc7Dw1EK5QyEiIiIiqvZYSFUSZ5POAuCy50RERERE5oCFVCWhSdIAAPxc/WSNg4iIiIiIWEhVCn9n/I27GXehEBR4weUFucMhIiIionIWGxsLQRDw+PHjYvs1aNAAy5cvlySm53n48CFcXV0RHx9fYfsoaV6eNnnyZIwfP77CYsrHQqoSiLsfBwDwqekDO6WdzNEQERERUVGioqLg4OCAvLw8Q1t6ejqUSiW6dOli1Pfo0aMQBAFXr15Fx44dce/ePTg5OQEANmzYgBo1apRLTBERERg4cGCRn3/11VcIDg6Go6OjSUXLokWL0K9fP3h5eSEyMhKCIBT7Kk3B9WxeSuKDDz7A+vXrcfPmTZP3ZwoWUpVA3N9PCineH0VERERk3kJCQpCeno4zZ84Y2o4ePQq1Wo3Tp08jMzPT0B4bGwsPDw/4+PjAysoKarUagiBIHnNmZiZ69eqFGTNmlHibrKwsrF27FqNHjwbw5CzQvXv3DK+6deti3rx5Rm316tUzbJ+bm1ui/ZQmL66urggNDUVUVFSJtykNFlKVgOa+BgALKSIiIiIAyNRmFvnK0eWUuG92XnaJ+pqiSZMm8PDwQGxsrKEtNjYWAwYMQMOGDXHixAmj9pCQEMPP+WeDYmNjMXLkSKSkpBjO5sydO/efODMzMWrUKDg4OKB+/fr46quvTIrxWRMmTMC0adPQoUOHEm/z008/wdLSEgEBAQAAe3t7qNVqw0uhUMDBwcHwftq0aRg0aBAWLVpkKB4BYOPGjWjbtq2h77Bhw5CUlGSUo6fPkuWfqdu/fz+aNWsGe3t79OrVC/fu3TOKr3///tiyZUuZ8vI8lhU6OpWLL7t/iXP3z6GVSyu5QyEiIiKS3YubXyzysy51umB199WG98HfByMrL6vQvm3d2mJ9r/WG97129EJyTnKBfhfCL5gUX3BwMGJiYjBt2jQAQExMDD744APo9XrExMSge/fuyM3NxcmTJ7Fy5coC23fs2BHLly/H7NmzceXKFQCAra0t9Ho9AODTTz/F/PnzMWPGDGzfvh1vv/02AgMD0bRpU5PiLIsjR46gbdu2Jm1z6NAhODo6Ijo6GqIoAnhyZmr+/Plo0qQJkpKSMHHiRERERGDv3r1FjpOZmYmlS5fi22+/hYWFBV577TVMnjwZmzZtMvRp3749bt++jYSEBHh6epbuIJ+DhVQlUMOmBoLqBckdBhERERGVQHBwMCZOnIi8vDxkZWUhLi4OgYGB0Ol0WLFiBQDg1KlTyMrKMpyRepqVlRWcnJwgCALUajUAQK/XIzU1FQDQp08fjB07FgAwdepUfPbZZ4iNjZW0kIqPj4eHh4dJ29jZ2eHf//43rKysDG2jRo0y/NygQQOsWLEC7du3R3p6Ouzt7QsdR6vVIioqCg0bNgQAjBs3DvPmzTPqU6dOHUOcLKSIiIiIiAD8MuyXIj9TWCiM3seGxRbZ10Iwvstl36B9ZYorX0hICDIyMnD69GkkJyfDx8cHrq6uCAoKwogRI5CRkYHY2FjUr18fDRo0MHn8F174ZxXn/GLr6cvhpJCVlQUbGxuTtmnZsqVREQUAcXFxiIyMhEajwaNHjwxn3W7duoXmzZsXOo6tra2hiAIAd3f3AsevUqkAwOietPLGQsrMLTuzDJYWlhjsMxge9qZV/URERERVka3SVva+xWnUqBHq1q2LmJgYJCcnIyjoyZVFarUa3t7eOH78OGJiYtC1a9dSja9UKo3eC4JgKECk4uzsjOTkgpdBFsfOznj16YyMDISGhiI0NBQbN26Ei4sLbt26hZ49exa7GEVhx59/qWC+R48eAQBcXFxMitEUXGzCjOXp8/Ddle/w9YWvkZabJnc4RERERFRCISEhiI2NRWxsLIKDgw3tQUFB2L9/P06dOlXoZX35rKysoNPpJIi0dPz9/XHp0qUyjfHHH3/gwYMHWLx4Mbp06YKmTZuW25m1ixcvQqlUwtfXt1zGKwwLKTN2LfkaMvMyYa+0R6MajeQOh4iIiIhKKCQkBMeOHYNGozGckQKeFFJff/01srOziy2kvLy8kJ6ejkOHDuHBgwdlvkQtJSUFGo3G6HXr1i0AQGJiIjQaDa5fvw4AuHDhguFSu6L07NkTv//+u8lnpZ5Wv359WFlZYeXKlbhx4wZ2796N+fPnl3q8px09ehRdunQxXOJXEVhImbG4pCfPj2rl0qrA9b5EREREZL5CQkKQlZWFRo0awc3NzdAeFBSEtLQ0NGzY0Oi5Ss/q2LEj3nrrLQwZMgQuLi745JNPyhRPbGws/P39jV6zZ88G8OQhwv7+/njjjTcAAIGBgfD398fu3buLHK9ly5Zo27Ytvv/++1LH5OLigg0bNmDbtm1o3rw5Fi9ejKVLl5Z6vKdt2bLFcDwVhfdImTFNkgYA4OfqJ2scRERERGQaLy+vAvftAEDdunULbQ8ODi7QvmbNGqxZswbAP6v23bhxAxYWxudCNBpNsbFs2LABGzZsKPLzyMhIREZGFjtGYWbNmoXJkyfjjTfeKBBTfHx8gRgK8+qrr+LVV181ans6D8/mJSIiAhEREUb9Bw4caNRnz549UCgUGDx4sAlHYzoWUmYs7v6TM1J8EC8RERERmZs+ffrg2rVruHPnTrFn16SWkZGB9evXw9KyYksdFlJmKjEjEYkZiVAICrR0bil3OEREREREBbz33ntyh1BAWFiYJPsx63uk8vLyMHPmTHh7e0OlUqFBgwaYN29ekcs7vvnmmxAEAcuXL5c20ApwJ/0OHKwc0LRW03JbipOIiIiIiMqHWZ+RWrJkCaKiovDNN9/A19cXZ86cwciRI+Hk5FSg+t21axd++eUXk5+wbK7auLXBsaHHkJxd+pVQiIiIiIioYph1IXXy5EkMGDAAffv2BfDkpr0tW7bgzJkzRv3u3LmDcePGYf/+/Ya+VYGFYIHaqtpyh0FERERERM8w60Kqc+fOiIqKwtWrV+Hj44Nz587h2LFjRpfu6fV6jBgxAlOmTCnxA7dycnKQk5NjeJ+amgoA0Gq10Gq15XoMptJqtRBFUfY4qpP8XDPn0mHOpcV8S485lx5zLi0p8p3/nUiv1xd5W0d1kr8qXX5OqGz0er3hO7dCYfyYoZLOa7MupKZOnYqUlBQ0bdoUCoUCOp0OCxcuNFoiccmSJbC0tMT48eNLPO6iRYswd+7cAu0HDhyAra389yNdz7uOpduXooVVC/RW9ZY7nGojOjpa7hCqHeZcWsy39Jhz6THn0qrIfFtaWkKtViM9PR25ubkVtp/KJi0tTe4QqoTc3FxkZWXhyJEjyMvLM/qspA8/NutC6rvvvsPGjRuxefNm+Pr6QqPRYMKECfDw8EB4eDh+++03fP755zh79iwEQSjxuNOnT8ekSZMM71NTU1GvXj2EhobC0dGxIg6lxLRaLQ7tPoQUMQVO7k7o07GPrPFUB1qtFtHR0ejRoweUSqXc4VQLzLm0mG/pMefSY86lJUW+s7Ozcfv2bdjb28PGxqZC9lGZiKKItLQ0ODg4mPS9lwqXnZ0NlUqFwMDAAvMr/2q15zHrQmrKlCmYNm0ahg4dCuDJE5QTEhKwaNEihIeH4+jRo0hKSkL9+vUN2+h0Orz//vtYvnx5gQeB5bO2toa1tXWBdqVSaRZ/fBN0CQCANuo2ZhFPdWEuv//qhDmXFvMtPeZcesy5tCoy3zqdDoIgwMLCosDDXquj/Mv58nNCZWNhYQFBEAqdwyWd02b9W8jMzCwwURQKhWEijRgxAufPn4dGozG8PDw8MGXKFOzfv1+OkMssT5+Hv/L+AgD4ufrJGwwRERERSSI2NhaCIODx48fF9mvQoIHZPOrn4cOHcHV1LfLkRUVr164ddu7cKcu+ATMvpPr164eFCxdiz549iI+Pxw8//IBly5bh5ZdfBgDUrl0bLVq0MHoplUqo1Wo0adJE5uhL5+rjq8hFLuyV9mhUo5Hc4RARERGRCaKiouDg4GB03016ejqUSiW6dOli1Pfo0aMQBAFXr15Fx44dce/ePTg5OQEANmzYgBo1apRLTBERERg4cGCRn3/11VcIDg6Go6NjiYq5fIsWLUK/fv3g5eWFyMhICIJQ7Ku0BVdRuZg1axamTZsm2+IbZl1IrVy5EoMHD8bYsWPRrFkzTJ48GW+++Sbmz58vd2gV5tz9cwCAVs6tYCGY9a+HiIiIiJ4REhKC9PR0o8f1HD16FGq1GqdPnzZayCA2NhYeHh7w8fGBlZUV1Gq1LPc/ZWZmolevXpgxY0aJt8nKysLatWsxevRoAMDkyZNx7949w6tu3bqYN2+eUVu9evXKNe6+ffsiJSVFtivRzPqbuoODA5YvX46EhARkZWXhzz//xIIFC2BlZVXkNvHx8ZgwYYJ0QZYzzX0NAKCVSyt5AyEiIiIyU/rMzKJfTz3i5rl9s7NL1NcUTZo0gYeHB2JjYw1tsbGxGDBgABo2bIgTJ04YtYeEhBh+zj8bFBsbi5EjRyIlJcVwNufpFaczMzMxatQoODg4oH79+vjqq69MivFZEyZMwLRp09ChQ4cSb/PTTz/B0tISAQEBAAB7e3uo1WrDS6FQwMHBwfBepVLh7bffhqurKxwdHdG1a1ecO3fOMN65c+cQEhICBwcHODo6ok2bNjhz5kyhuYiMjATw5JafPn36YMuWLWU6/tIy68UmqiNvR2+4K9zh7+ovdyhEREREZulK6zZFfmYXFIj6X35peH+1U2eIWVmF9rVt1w6e3/7H8P56t+7QJScX6Nfsj8smxRccHIyYmBhMmzYNABATE4MPPvgAer0eMTEx6N69O3Jzc3Hy5EmsXLmywPYdO3bE8uXLMXv2bFy5cuVJrLa2hkvYPv30U8yfPx8zZszA9u3b8fbbbyMwMBBNmzY1Kc6yOHLkCNq2bVuivqIoom/fvqhVqxb27t0LJycnfPnll+jWrRuuXr2KWrVqYfjw4fD398eaNWugUCig0WigVCoLzYW9vb1h7Pbt2+Pjjz+ukGN8HhZSZuatF95C/b/qo41r0X8giIiIiMh8BQcHY+LEicjLy0NWVhbi4uIQGBgInU6HFStWAABOnTqFrKwswxmpp1lZWcHJyQmCIECtVgN4smpf/rLcffr0wdixYwE8ee7qZ599htjYWEkLqfj4eHh4eJSob0xMDC5cuICkpCTDytlLly7Frl27sH37dowZMwa3bt3ClClTDMfQuHFjw/bP5uJpderUwa1bt6DX6yVfzZCFFBERERFVKk3O/lb0hwqF0Vuf48eK7vvMF+9Ghw6WJSyDkJAQZGRk4PTp00hOToaPjw9cXV0RFBSEESNGICMjA7Gxsahfvz4aNGhg8vgvvPCC4ef8AiMpKalcYi+prKysEj/f67fffkN6ejpq165dYIw///wTADBp0iSMHj0a3377Lbp3745//etfaNiw4XPHVqlU0Ov1yMnJgUqlMv1AyoCFFBERERFVKha2trL3LU6jRo1Qt25dxMTEIDk5GUFBQQAAtVoNb29vHD9+HDExMejatWupxn/2OUeCIEi+cp2zszOSC7kMsjB6vR7u7u5G943ly1+NLzIyEsOGDcOePXvw008/Yc6cOdi6dathte6iPHr0CLa2tpIXUYCZLzZBRERERFQZhYSEIDY2FrGxsQgODja0BwUFYf/+/Th16lShl/Xls7Kygk6nkyDS0vH398elS5dK1Ld169ZITEyEpaUlGjVqZPRydnY29PPx8cHEiRNx4MABvPLKK1i/fj2A4nNx8eJFtG7duuwHVAospIiIiIiIyllISAiOHTsGjUZjOCMFPCmkvv76a2RnZxdbSHl5eSE9PR2HDh3CgwcPjJZNL42UlBRoNBqj161btwAAiYmJ0Gg0uH79OgDgwoUL0Gg0ePToUZHj9ezZE7///nuJzkp1794dAQEBGDhwIPbv34/4+HicOHECM2fOxJkzZ5CVlYVx48YhNjYWCQkJOH78OE6fPo1mzZo9NxdHjx5FaGhoWVJTaiykiIiIiIjKWUhICLKystCoUSO4ubkZ2oOCgpCWloaGDRsW+1yljh074q233sKQIUPg4uKCTz75pEzxxMbGwt/f3+g1e/ZsAE8eIuzv74833ngDABAYGAh/f3/s3r27yPFatmyJtm3b4vvvv3/uvgVBwN69exEYGIhRo0bBx8cHQ4cORXx8PNzc3KBQKPDw4UO8/vrr8PHxQVhYGHr37m1Y8v3ZXOSv0nfnzh2cOHECI0eOLFNuSksQRVGUZc9mJDU1FU5OTkhJSYGjo6OssWi1Wuzduxd9+vQpcP0rVQzmXHrMubSYb+kx59JjzqUlRb6zs7Nx8+ZNeHt7l3hRg6osf9U+R0dHyVenK8revXsxefJkXLx4UZaYpkyZgpSUlFI9R6u4+VXS2oCLTRARERERkcn69OmDa9eu4c6dO8WeXasorq6umDx5suT7zcdCioiIiIiISuW9996Tbd9TpkyRbd8A75EiIiIiIiIyGQspIiIiIiIiE7GQIiIiIiKzxXXRqCKUx7xiIUVEREREZid/NcCyPj+JqDD586osq05ysQkiIiIiMjsKhQI1atRAUlISAMDW1haCIMgclXz0ej1yc3ORnZ1tNsufV0aiKCIzMxNJSUmoUaMGFApFqcdiIUVEREREZkmtVgOAoZiqzkRRRFZWFlQqVbUuKMtLjRo1DPOrtFhIEREREZFZEgQB7u7ucHV1hVarlTscWWm1Whw5cgSBgYF86HQZKZXKMp2JysdCioiIiIjMmkKhKJcvvpWZQqFAXl4ebGxsWEiZCV5gSUREREREZCIWUkRERERERCZiIUVERERERGQi3iOFfx7IlZqaKnMkT24kzMzMRGpqKq9/lQhzLj3mXFrMt/SYc+kx59JivqXHnEsnvyZ43kN7WUgBSEtLAwDUq1dP5kiIiIiIiMgcpKWlwcnJqcjPBfF5pVY1oNfrcffuXTg4OMi+Ln9qairq1auH27dvw9HRUdZYqgvmXHrMubSYb+kx59JjzqXFfEuPOZeOKIpIS0uDh4dHsQ8/5hkpABYWFqhbt67cYRhxdHTk/yQSY86lx5xLi/mWHnMuPeZcWsy39JhzaRR3JiofF5sgIiIiIiIyEQspIiIiIiIiE7GQMjPW1taYM2cOrK2t5Q6l2mDOpcecS4v5lh5zLj3mXFrMt/SYc/PDxSaIiIiIiIhMxDNSREREREREJmIhRUREREREZCIWUkRERERERCZiIUVERERERGQiFlIyWL16Nby9vWFjY4M2bdrg6NGjxfY/fPgw2rRpAxsbGzRo0ABRUVESRVr5LVq0CO3atYODgwNcXV0xcOBAXLlypdhtYmNjIQhCgdcff/whUdSVW2RkZIHcqdXqYrfhHC89Ly+vQufrO++8U2h/zm/THTlyBP369YOHhwcEQcCuXbuMPhdFEZGRkfDw8IBKpUJwcDB+//335467Y8cONG/eHNbW1mjevDl++OGHCjqCyqe4nGu1WkydOhUtW7aEnZ0dPDw88Prrr+Pu3bvFjrlhw4ZC5352dnYFH435e94cj4iIKJC3Dh06PHdczvGiPS/nhc1VQRDwySefFDkm57j0WEhJ7LvvvsOECRPw4YcfIi4uDl26dEHv3r1x69atQvvfvHkTffr0QZcuXRAXF4cZM2Zg/Pjx2LFjh8SRV06HDx/GO++8g1OnTiE6Ohp5eXkIDQ1FRkbGc7e9cuUK7t27Z3g1btxYgoirBl9fX6PcXbhwoci+nONlc/r0aaNcR0dHAwD+9a9/Fbsd53fJZWRkoFWrVli1alWhn3/88cdYtmwZVq1ahdOnT0OtVqNHjx5IS0srcsyTJ09iyJAhGDFiBM6dO4cRI0YgLCwMv/zyS0UdRqVSXM4zMzNx9uxZzJo1C2fPnsXOnTtx9epV9O/f/7njOjo6Gs37e/fuwcbGpiIOoVJ53hwHgF69ehnlbe/evcWOyTlevOfl/Nl5um7dOgiCgEGDBhU7Lue4xESSVPv27cW33nrLqK1p06bitGnTCu3/wQcfiE2bNjVqe/PNN8UOHTpUWIxVWVJSkghAPHz4cJF9YmJiRABicnKydIFVIXPmzBFbtWpV4v6c4+XrvffeExs2bCjq9fpCP+f8LhsA4g8//GB4r9frRbVaLS5evNjQlp2dLTo5OYlRUVFFjhMWFib26tXLqK1nz57i0KFDyz3myu7ZnBfm119/FQGICQkJRfZZv3696OTkVL7BVUGF5Ts8PFwcMGCASeNwjpdcSeb4gAEDxK5duxbbh3NcejwjJaHc3Fz89ttvCA0NNWoPDQ3FiRMnCt3m5MmTBfr37NkTZ86cgVarrbBYq6qUlBQAQK1atZ7b19/fH+7u7ujWrRtiYmIqOrQq5dq1a/Dw8IC3tzeGDh2KGzduFNmXc7z85ObmYuPGjRg1ahQEQSi2L+d3+bh58yYSExON5rC1tTWCgoKK/LsOFD3vi9uGipaSkgJBEFCjRo1i+6Wnp8PT0xN169bFSy+9hLi4OGkCrAJiY2Ph6uoKHx8fvPHGG0hKSiq2P+d4+fn777+xZ88e/N///d9z+3KOS4uFlIQePHgAnU4HNzc3o3Y3NzckJiYWuk1iYmKh/fPy8vDgwYMKi7UqEkURkyZNQufOndGiRYsi+7m7u+Orr77Cjh07sHPnTjRp0gTdunXDkSNHJIy28nrxxRfxn//8B/v378fXX3+NxMREdOzYEQ8fPiy0P+d4+dm1axceP36MiIiIIvtwfpev/L/dpvxdz9/O1G2ocNnZ2Zg2bRqGDRsGR0fHIvs1bdoUGzZswO7du7FlyxbY2NigU6dOuHbtmoTRVk69e/fGpk2b8PPPP+PTTz/F6dOn0bVrV+Tk5BS5Ded4+fnmm2/g4OCAV155pdh+nOPSs5Q7gOro2X8pFkWx2H89Lqx/Ye1UvHHjxuH8+fM4duxYsf2aNGmCJk2aGN4HBATg9u3bWLp0KQIDAys6zEqvd+/ehp9btmyJgIAANGzYEN988w0mTZpU6Dac4+Vj7dq16N27Nzw8PIrsw/ldMUz9u17abciYVqvF0KFDodfrsXr16mL7dujQwWiBhE6dOqF169ZYuXIlVqxYUdGhVmpDhgwx/NyiRQu0bdsWnp6e2LNnT7Ff7jnHy8e6deswfPjw597rxDkuPZ6RkpCzszMUCkWBf41JSkoq8K82+dRqdaH9LS0tUbt27QqLtap59913sXv3bsTExKBu3bomb9+hQwf+i04p2dnZoWXLlkXmj3O8fCQkJODgwYMYPXq0ydtyfpde/oqUpvxdz9/O1G3ImFarRVhYGG7evIno6Ohiz0YVxsLCAu3atePcLwV3d3d4enoWmzvO8fJx9OhRXLlypVR/2znHKx4LKQlZWVmhTZs2hlW18kVHR6Njx46FbhMQEFCg/4EDB9C2bVsolcoKi7WqEEUR48aNw86dO/Hzzz/D29u7VOPExcXB3d29nKOrHnJycnD58uUi88c5Xj7Wr18PV1dX9O3b1+RtOb9Lz9vbG2q12mgO5+bm4vDhw0X+XQeKnvfFbUP/yC+irl27hoMHD5bqH11EUYRGo+HcL4WHDx/i9u3bxeaOc7x8rF27Fm3atEGrVq1M3pZzXAJyrXJRXW3dulVUKpXi2rVrxUuXLokTJkwQ7ezsxPj4eFEURXHatGniiBEjDP1v3Lgh2traihMnThQvXbokrl27VlQqleL27dvlOoRK5e233xadnJzE2NhY8d69e4ZXZmamoc+zOf/ss8/EH374Qbx69ap48eJFcdq0aSIAcceOHXIcQqXz/vvvi7GxseKNGzfEU6dOiS+99JLo4ODAOV6BdDqdWL9+fXHq1KkFPuP8Lru0tDQxLi5OjIuLEwGIy5YtE+Pi4gwrxC1evFh0cnISd+7cKV64cEF89dVXRXd3dzE1NdUwxogRI4xWZz1+/LioUCjExYsXi5cvXxYXL14sWlpaiqdOnZL8+MxRcTnXarVi//79xbp164oajcbob3tOTo5hjGdzHhkZKe7bt0/8888/xbi4OHHkyJGipaWl+Msvv8hxiGaluHynpaWJ77//vnjixAnx5s2bYkxMjBgQECDWqVOHc7wMnvd3RRRFMSUlRbS1tRXXrFlT6Bic4/JjISWDL774QvT09BStrKzE1q1bGy3FHR4eLgYFBRn1j42NFf39/UUrKyvRy8uryP+hqCAAhb7Wr19v6PNszpcsWSI2bNhQtLGxEWvWrCl27txZ3LNnj/TBV1JDhgwR3d3dRaVSKXp4eIivvPKK+Pvvvxs+5xwvf/v37xcBiFeuXCnwGed32eUvGf/sKzw8XBTFJ0ugz5kzR1Sr1aK1tbUYGBgoXrhwwWiMoKAgQ/9827ZtE5s0aSIqlUqxadOmLGafUlzOb968WeTf9piYGMMYz+Z8woQJYv369UUrKyvRxcVFDA0NFU+cOCH9wZmh4vKdmZkphoaGii4uLqJSqRTr168vhoeHi7du3TIag3PcNM/7uyKKovjll1+KKpVKfPz4caFjcI7LTxDF/39XNxEREREREZUI75EiIiIiIiIyEQspIiIiIiIiE7GQIiIiIiIiMhELKSIiIiIiIhOxkCIiIiIiIjIRCykiIiIiIiITsZAiIiIiIiIyEQspIiIiIiIiE7GQIiIiKiNBELBr1y65wyAiIgmxkCIiokotIiICgiAUePXq1Uvu0IiIqAqzlDsAIiKisurVqxfWr19v1GZtbS1TNEREVB3wjBQREVV61tbWUKvVRq+aNWsCeHLZ3Zo1a9C7d2+oVCp4e3tj27ZtRttfuHABXbt2hUqlQu3atTFmzBikp6cb9Vm3bh18fX1hbW0Nd3d3jBs3zujzBw8e4OWXX4atrS0aN26M3bt3V+xBExGRrFhIERFRlTdr1iwMGjQI586dw2uvvYZXX30Vly9fBgBkZmaiV69eqFmzJk6fPo1t27bh4MGDRoXSmjVr8M4772DMmDG4cOECdu/ejUaNGhntY+7cuQgLC8P58+fRp08fDB8+HI8ePZL0OImISDqCKIqi3EEQERGVVkREBDZu3AgbGxuj9qlTp2LWrFkQBAFvvfUW1qxZY/isQ4cOaN26NVavXo2vv/4aU6dOxe3bt2FnZwcA2Lt3L/r164e7d+/Czc0NderUwciRI7FgwYJCYxAEATNnzsT8+fMBABkZGXBwcMDevXt5rxYRURXFe6SIiKjSCwkJMSqUAKBWrVqGnwMCAow+CwgIgEajAQBcvnwZrVq1MhRRANCpUyfo9XpcuXIFgiDg7t276NatW7ExvPDCC4af7ezs4ODggKSkpNIeEhERmTkWUkREVOnZ2dkVuNTueQRBAACIomj4ubA+KpWqROMplcoC2+r1epNiIiKiyoP3SBERUZV36tSpAu+bNm0KAGjevDk0Gg0yMjIMnx8/fhwWFhbw8fGBg4MDvLy8cOjQIUljJiIi88YzUkREVOnl5OQgMTHRqM3S0hLOzs4AgG3btqFt27bo3LkzNm3ahF9//RVr164FAAwfPhxz5sxBeHg4IiMjcf/+fbz77rsYMWIE3NzcAACRkZF466234Orqit69eyMtLQ3Hjx/Hu+++K+2BEhGR2WAhRUREld6+ffvg7u5u1NakSRP88ccfAJ6sqLd161aMHTsWarUamzZtQvPmzQEAtra22L9/P9577z20a9cOtra2GDRoEJYtW2YYKzw8HNnZ2fjss88wefJkODs7Y/DgwdIdIBERmR2u2kdERFWaIAj44YcfMHDgQLlDISKiKoT3SBEREREREZmIhRQREREREZGJeI8UERFVabyCnYiIKgLPSBEREREREZmIhRQREREREZGJWEgRERERERGZiIUUERERERGRiVhIERERERERmYiFFBERERERkYlYSBEREREREZmIhRQREREREZGJ/h8bxMpc6r8KKgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化对比\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(no_l1_train, label=\"No L1 (Train)\")\n",
    "plt.plot(no_l1_test, label=\"No L1 (Test)\")\n",
    "plt.plot(l1_train, \"--\", label=\"With L1 (Train)\")\n",
    "plt.plot(l1_test, \"--\", label=\"With L1 (Test)\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Accuracy (%)\")\n",
    "plt.title(\"L1 Regularization Effect on MNIST\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "from torch.utils.data import DataLoader\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 数据准备\n",
    "transform = transforms.Compose(\n",
    "    [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]\n",
    ")\n",
    "\n",
    "train_set = datasets.MNIST(\"data\", train=True, download=True, transform=transform)\n",
    "test_set = datasets.MNIST(\"data\", train=False, download=True, transform=transform)\n",
    "\n",
    "train_loader = DataLoader(train_set, batch_size=64, shuffle=True)\n",
    "test_loader = DataLoader(test_set, batch_size=64, shuffle=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 定义全连接网络\n",
    "class MNISTClassifier(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(784, 512)\n",
    "        self.fc2 = nn.Linear(512, 256)\n",
    "        self.fc3 = nn.Linear(256, 10)\n",
    "        self.relu = nn.ReLU()\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.view(-1, 784)\n",
    "        x = self.relu(self.fc1(x))\n",
    "        x = self.relu(self.fc2(x))\n",
    "        return self.fc3(x)\n",
    "\n",
    "\n",
    "# 训练函数（增加L2正则化选项）\n",
    "def train_model(use_l2=False):\n",
    "    model = MNISTClassifier()\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    weight_decay = 0.001 if use_l2 else 0.0  # L2正则化系数\n",
    "    optimizer = optim.Adam(model.parameters(), lr=0.001, weight_decay=weight_decay)\n",
    "\n",
    "    train_losses, test_losses = [], []\n",
    "    train_accs, test_accs = [], []\n",
    "\n",
    "    for epoch in range(15):\n",
    "        # 训练阶段\n",
    "        model.train()\n",
    "        epoch_loss = 0.0\n",
    "        correct = 0\n",
    "        for images, labels in train_loader:\n",
    "            optimizer.zero_grad()\n",
    "            outputs = model(images)\n",
    "            loss = criterion(outputs, labels)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            epoch_loss += loss.item()\n",
    "            correct += (outputs.argmax(1) == labels).sum().item()\n",
    "\n",
    "        train_losses.append(epoch_loss / len(train_loader))\n",
    "        train_accs.append(correct / len(train_set))\n",
    "\n",
    "        # 测试阶段\n",
    "        model.eval()\n",
    "        test_loss = 0.0\n",
    "        correct = 0\n",
    "        with torch.no_grad():\n",
    "            for images, labels in test_loader:\n",
    "                outputs = model(images)\n",
    "                test_loss += criterion(outputs, labels).item()\n",
    "                correct += (outputs.argmax(1) == labels).sum().item()\n",
    "\n",
    "        test_losses.append(test_loss / len(test_loader))\n",
    "        test_accs.append(correct / len(test_set))\n",
    "\n",
    "        print(\n",
    "            f'Epoch {epoch+1} | {\"L2\" if use_l2 else \"No Reg\"} | '\n",
    "            f\"Train Acc: {train_accs[-1]:.4f} | Test Acc: {test_accs[-1]:.4f}\"\n",
    "        )\n",
    "\n",
    "    return train_losses, test_losses, train_accs, test_accs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 | No Reg | Train Acc: 0.9062 | Test Acc: 0.9452\n",
      "Epoch 2 | No Reg | Train Acc: 0.9579 | Test Acc: 0.9653\n",
      "Epoch 3 | No Reg | Train Acc: 0.9681 | Test Acc: 0.9702\n",
      "Epoch 4 | No Reg | Train Acc: 0.9737 | Test Acc: 0.9672\n",
      "Epoch 5 | No Reg | Train Acc: 0.9774 | Test Acc: 0.9768\n",
      "Epoch 6 | No Reg | Train Acc: 0.9813 | Test Acc: 0.9731\n",
      "Epoch 7 | No Reg | Train Acc: 0.9824 | Test Acc: 0.9742\n",
      "Epoch 8 | No Reg | Train Acc: 0.9850 | Test Acc: 0.9680\n",
      "Epoch 9 | No Reg | Train Acc: 0.9853 | Test Acc: 0.9733\n",
      "Epoch 10 | No Reg | Train Acc: 0.9875 | Test Acc: 0.9720\n",
      "Epoch 11 | No Reg | Train Acc: 0.9888 | Test Acc: 0.9735\n",
      "Epoch 12 | No Reg | Train Acc: 0.9883 | Test Acc: 0.9780\n",
      "Epoch 13 | No Reg | Train Acc: 0.9895 | Test Acc: 0.9784\n",
      "Epoch 14 | No Reg | Train Acc: 0.9903 | Test Acc: 0.9772\n",
      "Epoch 15 | No Reg | Train Acc: 0.9908 | Test Acc: 0.9800\n",
      "Epoch 1 | L2 | Train Acc: 0.9023 | Test Acc: 0.9524\n",
      "Epoch 2 | L2 | Train Acc: 0.9511 | Test Acc: 0.9536\n",
      "Epoch 3 | L2 | Train Acc: 0.9596 | Test Acc: 0.9631\n",
      "Epoch 4 | L2 | Train Acc: 0.9641 | Test Acc: 0.9641\n",
      "Epoch 5 | L2 | Train Acc: 0.9681 | Test Acc: 0.9614\n",
      "Epoch 6 | L2 | Train Acc: 0.9701 | Test Acc: 0.9705\n",
      "Epoch 7 | L2 | Train Acc: 0.9716 | Test Acc: 0.9686\n",
      "Epoch 8 | L2 | Train Acc: 0.9721 | Test Acc: 0.9639\n",
      "Epoch 9 | L2 | Train Acc: 0.9743 | Test Acc: 0.9677\n",
      "Epoch 10 | L2 | Train Acc: 0.9733 | Test Acc: 0.9729\n",
      "Epoch 11 | L2 | Train Acc: 0.9756 | Test Acc: 0.9726\n",
      "Epoch 12 | L2 | Train Acc: 0.9756 | Test Acc: 0.9700\n",
      "Epoch 13 | L2 | Train Acc: 0.9756 | Test Acc: 0.9665\n",
      "Epoch 14 | L2 | Train Acc: 0.9756 | Test Acc: 0.9710\n",
      "Epoch 15 | L2 | Train Acc: 0.9769 | Test Acc: 0.9756\n"
     ]
    }
   ],
   "source": [
    "# 训练两种模型\n",
    "no_l2_train_loss, no_l2_test_loss, no_l2_train_acc, no_l2_test_acc = train_model(\n",
    "    use_l2=False\n",
    ")\n",
    "l2_train_loss, l2_test_loss, l2_train_acc, l2_test_acc = train_model(use_l2=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化结果\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "# 准确率对比\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(no_l2_train_acc, label=\"No L2 (Train)\", linestyle=\"--\", color=\"red\")\n",
    "plt.plot(no_l2_test_acc, label=\"No L2 (Test)\", color=\"red\")\n",
    "plt.plot(l2_train_acc, label=\"With L2 (Train)\", linestyle=\"--\", color=\"blue\")\n",
    "plt.plot(l2_test_acc, label=\"With L2 (Test)\", color=\"blue\")\n",
    "plt.title(\"Accuracy Comparison\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "\n",
    "# 损失对比\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(no_l2_train_loss, label=\"No L2 (Train)\", linestyle=\"--\", color=\"red\")\n",
    "plt.plot(no_l2_test_loss, label=\"No L2 (Test)\", color=\"red\")\n",
    "plt.plot(l2_train_loss, label=\"With L2 (Train)\", linestyle=\"--\", color=\"blue\")\n",
    "plt.plot(l2_test_loss, label=\"With L2 (Test)\", color=\"blue\")\n",
    "plt.title(\"Loss Comparison\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mytorchenv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.21"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
